We are looking for a highly motivated and skilled Data Scientist/AI Specialist to join our innovative team in the Annapolis Junction, MD area.

The ideal candidate will possess a strong background in programming, statistical analysis, machine learning, and data preparation, ensuring effective data analysis and informed decision-making.

What you’ll do:

• Develop and implement data-driven solutions using programming languages such as Python and R
• Conduct statistical analyses, including regression, hypothesis testing, and distribution analysis, to interpret data accurately
• Design and train machine learning models using frameworks like TensorFlow and Scikit-Learn for predictive analytics and automation
• Perform data wrangling to clean and organize raw datasets, making them ready for meaningful analysis
• Collaborate with cross-functional teams to understand business needs and deliver actionable insights

What you’ll need:

• 14 years of experience in software engineering in similar programs and contracts utilizing programming languages such as: C++, Python, or Go Lang
• Bachelor’s degree in Systems Engineering, Computer Science, Computer Engineer, or a related field
  • 4 years of relevant experience can substitute for a Bachelor’s degree

Bonus points if you have:

• Strong foundation in statistics and probability, including regression analysis and hypothesis testing
• Hands-on experience with machine learning frameworks such as SageMaker, TensorFlow and Scikit-Learn
• Expertise in data wrangling to prepare raw data for analysis
• Excellent problem-solving and analytical skills
• Familiarity with libraries like NumPy, pandas, and advanced visualization tools
• Experience in creating impactful data visualizations and reports for stakeholders
• Ability to work independently and in a collaborative team environment

Security Clearance:

• Must have an active TS/SCI with polygraph level clearance

#LI-DataAI

#clearancejobs

Build AI that matters. We ship production GenAI apps for confidential federal programs across defense, national security, public safety, civilian, and military health where reliability, privacy, and safety aren’t optional. AFS is a technology company within global Accenture and a Glassdoor Top 100 Best Place to Work. You’ll join a collaborative, inclusive community with handson growth, certifications, and industry training. We ship in weeks, not quarters, and measure success with latency, reliability, safety, and cost. 

Confidentiality matters: We don’t disclose program details publicly. If you advance, we’ll share specifics during the process. 

Role Overview 

You’ll turn mission needs into secure, reliable, and scalable GenAI applications no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You’ll own the end-to-end path from use case evaluation → production deployment → operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale. 

What You’ll Do (Day to Day) 

• Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost. 

• Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies. 

• Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma). 

• LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback. 

• RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IRstyle evals (e.g., NDCG) to maximize signal to noise. 

• Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails. 

• SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend. 

• Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission team not one-off projects. 

• Operate in real world constraints: Deliver into hybrid, restricted, or air gapped environments with Zero Trust principles and audit ready controls. 

You'll Thrive Here If you have 

• End-to-end ownership of production systems: integration → deployment → observability → incident response. 

• Hands-on experience with LLMs, transformer based apps, and RAG in production. 

• Strong Python 

• Experience with vector search and retrieval (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma) and grounding AI in enterprise/mission data. 

• U.S. Citizenship 

Nice to Have 

• Integration with leading cloud AI services or on prem inference stacks  

• Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLM Ops. 

• Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance. 

• Experience implementing tool using agents for API integration and external data access. 

• Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell). 

• Prior work in regulated/secure environments (e.g., ATO, STIGs, Zero Trust) with fast shipping. 

• Familiarity with NVIDIA AI Foundations, OpenAI ChatGPT, and AI assisted dev tools (Cursor, Windsurf, Claude). 

• Contributions to internal frameworks or opensource; mentorship of engineers. 

• Clear communication with engineers, PMs, and security/compliance stakeholders. 

The work: 

Key Responsibilities:

Accenture Federal Services is seeking a Sr. Python Developer to join our team and support our client in Ft. Meade, MD.

What you’ll do:

• Develop innovative approaches to glean meaning and value from large datasets- working collaboratively and iteratively throughout the data-science lifecycle
• Create qualitative and quantitative methods to characterize datasets in various states to optimize analytical workflows
• Identify new or existing algorithms, machine learning, or artificial intelligence technologies to help solve technical and operational problems
• Employ analytic modeling, scripting, and/or programming to aggregate data and construct usable intelligence from different data sets and sources concerning customer requirements
• Evaluate, document, and share research processes and results with peers, customers, and leadership

What you’ll need:

• A Doctoral degree plus 4 years of relevant experience, OR Master's degree plus 6 years of relevant experience, OR a Bachelor's degree plus 8 years of relevant experience, OR an Associate's degree plus 10 years of relevant experience, OR a High School diploma/GED plus 12 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position
• Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field
• 6 years of programming experience in Python

Clearance:

Must have a TS/SCI with polygraph level clearance

The work: 

• Develop MLOps frameworks and workflows for a variety of domains and applications 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom AI/ML algorithms that translate into mission value 

• Conduct experiments, evaluate model performance, and fine-tune algorithms to improve accuracy and efficiency

• Collaborate with cross-functional teams to integrate AI/ML models into products and services

Here’s what you need: 

• Hands-on experience with scripting languages such as Python, JavaScript or Rust 
• Familiar with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC
• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP 
• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 
• Experience working with and managing processing of large datasets and computational or analytic jobs  
• US Citizenship (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required  

Security Clearance: 

• Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

The work: 

• Develop MLOps frameworks and workflows for a variety of domains and applications 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom AI/ML algorithms that translate into mission value 

• Conduct experiments, evaluate model performance, and fine-tune algorithms to improve accuracy and efficiency

• Collaborate with cross-functional teams to integrate AI/ML models into products and services

Here’s what you need: 

• Hands-on experience with scripting languages such as Python, Javascript or Rust 

• Hands-on experience with developing machine learning models at scale from inception to business impact 

• Hands-on experience with any major deep learning framework and libraries (Tensorflow, PyTorch, HuggingFace) 

• Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC 

• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP 

• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 

• Experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment  

• Experience working with and managing processing of large datasets and computational or analytic jobs  

• Experience monitoring and triaging issues with data processes and tools   

• Must be a U.S. Citizen (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required  

Accenture Federal Services is looking for a highly motivated and skilled Data Scientist/AI Specialist to join our innovative team. The ideal candidate will possess a strong background in programming, statistical analysis, machine learning, and data preparation, ensuring effective data analysis and informed decision-making.

What you’ll do:

• Develop and implement data-driven solutions using programming languages such as Python and R
• Conduct statistical analyses, including regression, hypothesis testing, and distribution analysis, to interpret data accurately
• Design and train machine learning models using frameworks like TensorFlow and Scikit-Learn for predictive analytics and automation
• Perform data wrangling to clean and organize raw datasets, making them ready for meaningful analysis
• Collaborate with cross-functional teams to understand business needs and deliver actionable insights

What you’ll need:

• Bachelor's degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science)
  • 4 years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree
  • A PhD from an accredited college or university in a quantitative discipline can be substituted for four 4 years of experience
• 14 years of experience in software engineering in similar programs and contracts 
• 5 years of experience programming with data analysis software such as R, Python, SAS, or MATLAB

Bonus points if you have:

• Familiarity with libraries like NumPy, pandas, and advanced visualization tools
• Experience analyzing datasets and developing analytics 
• Experience in creating impactful data visualizations and reports for stakeholders
• Ability to work independently and in a collaborative team environment
• Strong foundation in statistics and probability, including regression analysis and hypothesis testing
• Hands-on experience with machine learning frameworks such as TensorFlow and Scikit-Learn
• Expertise in data wrangling to prepare raw data for analysis
• Excellent problem-solving and analytical skills

Security Clearance:

• Must have an active TS/SCI with polygraph level clearance

#clearancejobs

We are seeking a highly skilled AI/ML Engineer to join our team and support our client in the Annapolis Junction, MD area.

This role focuses on developing and deploying AI/ML solutions while ensuring secure, scalable, and efficient operations through automation, containerization, and cloud-native strategies. The ideal candidate is a fast learner with a passion for innovation and adaptability.

What you’ll be doing:

• Develop and deploy AI/ML models, including deep learning, LLM fine-tuning, and retrieval-augmented generation (RAG)
• Build and manage containerized applications using Docker and Kubernetes (K8s)
• Automate CI/CD pipelines for secure and efficient delivery
• Leverage AWS services like Bedrock, SageMaker, and general cloud architecture for scalable deployments
• Build secure Linux-based systems and applications, including web apps (Flask/Django) and databases (e.g., graph databases)

What you’ll need:

• Experience in Python and Linux environments
• Experience with Docker, Kubernetes, and/or CI/CD pipelines
• Experience with and knowledge of AI/ML principles, including model development and evaluation
• Familiarity with web app development and database management

Bonus points if you have:

• Experience with NVIDIA NIMs, NeMo, and agentic AI frameworks (AutoGen, CrewAI, LangGraph)
• Knowledge of AWS cloud services
• Knowledge of advanced DevSecOps tools for security and vulnerability assessments
• Exposure to API integrations and prompt engineering

Security Clearance:

• Must have an active TS/SCI with polygraph level clearance

The work: 

Key Responsibilities:

We are entering a new chapter in technology - one shaped by a generalization of AI. Accenture is a trusted industry leader at the forefront of AI innovation, providing clients with scalable AI and generative AI solutions for reinvention - to solve new problems, create new inventions, change how we work and live, and transform industries and governments.   

Seeking a Generative AI Engineer with the technical acumen and problem-solving ability to identify and develop new opportunities for the use of AI agents to solve real-world mission-critical problems for our clients. You will have expertise in the key components to power GenAI adoption - including rapid prototyping of custom applications, search capabilities such as R.A.G, Prompt Engineering Expertise, Large Language Model Expertise (Selection, fine tuning, and deployment) and GenAI Use Case evaluation. You will collaborate with a community of GenAI leaders, software and systems engineers, data scientists, solutions architects, and product owners to deliver innovative world-class solutions that will transform our clients ecosystem and reinvent the way they operate. 

The Work: 

• Design and build GenAI use cases based on client's needs and specific data    

• Understanding of Agentic AI frameworks and design and when they are appropriate for use 

• Develop and deploy generative AI models using platforms such as Amazon Kendra, Google Vertex AI, and AWS Bedrock. 

• Implement large language models (LLMs) and transformer models to create advanced AI applications. 

• Integrate solutions with cloud-based AI services such as Google Cloud Document AI, Google Cloud Gemini, Google Cloud Gemma, and Google Cloud Generative AI Language Models. 

• Utilize NVIDIA AI Foundations and OpenAI ChatGPT to develop and refine AI applications. 

• Leverage Databricks LLM and Databricks Mosaic AI for data analysis and AI model development. 

• Show your curiosity, motivation, and passion for experimentation and continuous learning to drive AI innovation that is applicable across enterprise client domains. 

Here's what you need: 

• Experience working with modern generative AI capabilities including large language models, transformer models, and retrieval-augmented generation 

• Expertise in AI platforms (e.g., Amazon Kendra, Google Vertex AI, AWS Bedrock, and Azure AI, etc.) 

• Experience with Google Cloud services (e.g., Document AI, Gemini, Gemma, Generative AI Language Models) 

• Experience building RAG systems with vector databases (e.g., Pinecone, Weaviate, ChromaDB, FAISS) 

• Experience using modern software languages (e.g., Python, SQL, Go) to develop generative AI solutions  

Bonus points if you have: 

• Strong understanding of AI ethics, responsible AI, and generative AI data ethics. 

• Experience in LLM fine-tuning and LLMOps. 

• Ability to author and validate prompts for AI models. 

• Familiarity with NVIDIA AI Foundations and OpenAI ChatGPT. 

• Experience with AI-assisted development platforms (e.g., Cursor, Windsurf, Claude, etc.). 

• Experience implementing tool-using AI agents to assist in API integration - enabling agent access to external data sources and web services. 

• Experience using deployment/virtualization tools in containerized environments (e.g., VMware, Docker, Kubernetes) 

• Proficiency in scripting with Linux Bash, PowerShell, or equivalent automation tools 

Security Clearance:

• Active Top Secret or TS/SCI or TS/SCI with polygraph clearance

Act as the primary bridge between mission stakeholders and our highly technical machine learning development team. You will be responsible for translating complex operational challenges into well-defined requirements for AI/ML models. Your work is critical to ensuring that the models we build are not just technically sound, but that they deliver tangible, mission-relevant outcomes. This role requires a unique blend of business acumen, analytical skills, and a solid understanding of artificial intelligence concepts.

Responsibilities:

• Elicit, analyze, and document business and functional requirements from mission stakeholders for new AI/ML models and features
• Translate mission problems into clear, unambiguous user stories and technical specifications for the machine learning development team
• Define acceptance criteria for AI/ML models, including required inputs, desired outputs, and key performance metrics (e.g., accuracy, precision, latency)
• Work closely with data scientists and ML engineers to ensure a clear understanding of the business problem and the proposed technical solution
• Facilitate communication between technical and non-technical teams, explaining complex AI/ML concepts in understandable terms
• Assist in creating test cases and participate in user acceptance testing (UAT) to validate that model outputs meet the defined business needs
• Maintain the product backlog for the MaaS platform, prioritizing features and user stories in collaboration with the team lead

Must have:

• 3+ years of experience as a Business Analyst, Systems Analyst, or in a similar requirements-focused role
• Experience in requirements elicitation, analysis, and documentation in an Agile/Scrum environment
• Solid understanding of fundamental AI/ML concepts (e.g., supervised vs. unsupervised learning, training data, model evaluation) 
• Excellent communication and interpersonal skills, with the ability to facilitate meetings and build consensus between diverse stakeholder groups
• Strong analytical and problem-solving abilities

Security Clearance

• Active TS or TS/SCI clearance

The work: 

• Develop, train, finetune and evaluate computer vision models in a wide range of topics, including geospatial, biometrics, 3D vision, semantic extraction, etc. 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom Computer Vision (CV) algorithms that translate into mission value 

• Create tools to provide feedback from production ML models and data processes  

• Assist in the development and optimization of computer vision models using deep learning frameworks (e.g., PyTorch, TensorFlow). 

• Collaborate with other scientists and engineers to build and deploy models for tasks such as object detection, image segmentation, classification, and tracking

• Stay up to date with the latest research and advancements in the field of computer vision and deep learning

• Participate in data collection, preprocessing, and augmentation processes to ensure high-quality datasets

• Conduct experiments, analyze results, and contribute to the improvement of model accuracy and efficiency

• Assist in the integration of computer vision algorithms into production systems and applications 

• Document code, methodologies, and experimental results

Here’s what you need: 

• Hands-on experience with computer vision libraries (e.g., OpenCV) and deep learning frameworks (e.g., PyTorch, TensorFlow)

• Proficiency with programming languages such as Python, C/C++ or Rust 

• Strong understanding of CNNs, transformers and other advanced architectures and their applications in computer vision tasks

• Strong analytical and problem-solving skills

• Ability to work collaboratively in a team environment and take direction from senior team members

• Hands-on experience with developing computer vision models at scale from inception to business impact 

• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 

• U.S. Citizenship (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required 
• Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC
• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP
• Hands on experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment 
• Hands-on experience with other image modalities (SAR, IR, HSI, Lidar, Sonar) 

Security Clearance: 

• Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

Build AI that matters. We ship production GenAI apps for confidential federal programs across defense, national security, public safety, civilian, and military health where reliability, privacy, and safety aren’t optional. AFS is a technology company within global Accenture and a Glassdoor Top 100 Best Place to Work. You’ll join a collaborative, inclusive community with handson growth, certifications, and industry training. We ship in weeks, not quarters, and measure success with latency, reliability, safety, and cost. 

Confidentiality matters: We don’t disclose program details publicly. If you advance, we’ll share specifics during the process. 

Role Overview 

You’ll turn mission needs into secure, reliable, and scalable GenAI applications no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You’ll own the end-to-end path from use case evaluation → production deployment → operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale. 

What You’ll Do (Day to Day) 

• Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost. 

• Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies. 

• Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma). 

• LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback. 

• RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IRstyle evals (e.g., NDCG) to maximize signal to noise. 

• Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails. 

• SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend. 

• Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission team not one-off projects. 

• Operate in real world constraints: Deliver into hybrid, restricted, or air gapped environments with Zero Trust principles and audit ready controls. 

You'll Thrive Here If you have 

• End-to-end ownership of production systems: integration → deployment → observability → incident response. 

• Hands-on experience with LLMs, transformer based apps, and RAG in production. 

• Strong Python 

• Experience with vector search and retrieval (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma) and grounding AI in enterprise/mission data. 

• U.S. Citizenship 

Nice to Have 

• Integration with leading cloud AI services or on prem inference stacks  

• Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLM Ops. 

• Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance. 

• Experience implementing tool using agents for API integration and external data access. 

• Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell). 

• Prior work in regulated/secure environments (e.g., ATO, STIGs, Zero Trust) with fast shipping. 

• Familiarity with NVIDIA AI Foundations, OpenAI ChatGPT, and AI assisted dev tools (Cursor, Windsurf, Claude). 

• Contributions to internal frameworks or opensource; mentorship of engineers. 

• Clear communication with engineers, PMs, and security/compliance stakeholders. 

As the Manager for the AI/ML Models as a Service (MaaS) team, you will lead a specialized group of developers and engineers dedicated to productionizing machine learning for the DoD. Your mission is to build and manage a centralized platform that provides access to pre-trained and custom-built AI/ML models, simplifying their integration and accelerating the delivery of AI-powered capabilities across the enterprise . This is a strategic, hands-on leadership role where you will define the vision for our MaaS offerings and oversee the entire lifecycle of model development, deployment, and operations.

Responsibilities:

• Lead, mentor, and manage a high-performing team of ML modeling developers and MLOps engineers.
• Define and execute the technical strategy for the MaaS platform, including the frameworks for model training, versioning, deployment, and monitoring.
• Oversee the design, development, and deployment of a diverse portfolio of machine learning models to solve complex mission challenges.
• Establish and enforce robust MLOps practices to ensure automated, reliable, and scalable CI/CD pipelines for machine learning models.
• Architect the service layer for the MaaS platform, ensuring models are exposed via secure, scalable, and well-documented APIs.
• Collaborate with data scientists, data engineers, and mission stakeholders to identify use cases and translate requirements into production-ready models.
• Implement governance, security, and ethical AI standards across the entire model lifecycle.
• Manage project timelines, resource allocation, and stakeholder communication for all MaaS initiatives.

Required Qualifications:

• 8+ years of experience in data science or machine learning engineering, with at least 3 years in a technical leadership or management role.
• Deep expertise in developing and deploying ML models using common frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Proven experience building and maintaining production ML systems in a cloud environment (AWS, Azure, GCP).
• Strong understanding of MLOps principles and hands-on experience with relevant tools (e.g., MLflow, Kubeflow, AWS SageMaker, Azure ML).
• Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD tools.
• Experience with programming skills in Python and familiarity with software engineering best practices. 
• US Citizenship (No Dual Citizenship)

Preferred Qualifications:

• Direct experience building a Model-as-a-Service or Machine-Learning-as-a-Service platform.
• Experience with ML platforms like Databricks or AWS SageMaker AI.
• Familiarity with Infrastructure-as-Code (IaC) tools like Terraform.
• Experience working in a high-security DoD or Intelligence Community environment.
• Demonstrated success leading teams that deliver complex, data-driven software projects.

Security Clearance:

• Active TS or TS/SCI Clearance

Lead the end-to-end technical design, development, and implementation of an agentic AI system to orchestrate user queries across heterogeneous data sources.  The architect is responsible for defining the overall system architecture, selecting and governing AI/ML and platform technologies, and ensuring the solution is scalable, secure, and production-ready. The architect partners closely with development, DevOps and data engineering teams to translate project requirements into an extensible AI architecture.

Must have experience designing and deploying LLM-based systems, including RAG, tool-calling, and multi-agent patterns, as well as a strong understanding of modern AI architectures such as embeddings, vector search, orchestration layers, memory, and reasoning workflows.  Proven ability to evaluate models, manage prompt strategies, and design system for traceability and reliability.

Must have:

• 8 years of experience with AI systems and Agentic systems
• 4 years experience with AI and/or Data
• Experience with Python, agent frameworks, data engineering, APIs/microservices, vector databases, SQL engines, distributed systems, cloud services, RAG
• Understanding of DevOps tools and principals

Nice to have:

• Experience with Model Context Protocol
• Bachelors degree

Security Clearance:

• Active TS/SCI with polygraph clearance

The work:  

As a Consumer Product Data Scientist, you will be using the Databricks platform to analyze complex datasets, identify trends, uncover patterns, and provide meaningful interpretations for a highly visible online product. The ideal candidate will possess a robust background in data analytics, hands-on experience with Databricks, and a proven track record in consumer research and retail analytics, with a strong preference for incorporating AI solutions. You will leverage skills in big data collection, cleaning, and exploratory analysis to discover patterns, anomalies, and relationships using statistical methods and visualization techniques. Requires strong experience applying statistical tests and models to validate hypotheses and identify significant trends in customer retention and churn. 

Here's what you need:

• 10+ years of experience in data analytics to drive impactful insights and solutions.
• Must have hands-on experience within Databricks environment.
• Must have hands-on experience developing and maintaining analytics and dashboards to support client decision-making.
• Must have experience with consumer research and retail analytics.
• Demonstrates successful outcomes with quantifiable metrics. 
• Incorporate success in integrating AI-driven solutions into analytics projects (strongly desired).

Eligibility Requirements: 

US Citizenship required.

Location: Washington, DC

#LI-DataAI

The work: 

As a Senior AIOps Engineer, you will be the technical architect and senior implementer driving the client's AI Operations initiative.

Key Responsibilities:

Build AI that matters. We ship production GenAI apps for confidential federal programs across defense, national security, public safety, civilian, and military health where reliability, privacy, and safety aren’t optional. AFS is a technology company within global Accenture and a Glassdoor Top 100 Best Place to Work. You’ll join a collaborative, inclusive community with handson growth, certifications, and industry training. We ship in weeks, not quarters, and measure success with latency, reliability, safety, and cost. 

Confidentiality matters: We don’t disclose program details publicly. If you advance, we’ll share specifics during the process. 

Role Overview 

You’ll turn mission needs into secure, reliable, and scalable GenAI applications no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You’ll own the end-to-end path from use case evaluation → production deployment → operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale. 

What You’ll Do (Day to Day) 

• Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost. 

• Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies. 

• Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma). 

• LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback. 

• RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IRstyle evals (e.g., NDCG) to maximize signal to noise. 

• Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails. 

• SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend. 

• Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission team not one-off projects. 

• Operate in real world constraints: Deliver into hybrid, restricted, or air gapped environments with Zero Trust principles and audit ready controls. 

You'll Thrive Here If you have 

• End-to-end ownership of production systems: integration → deployment → observability → incident response. 

• Hands-on experience with LLMs, transformer based apps, and RAG in production. 

• Strong Python 

• Experience with vector search and retrieval (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma) and grounding AI in enterprise/mission data. 

• U.S. Citizenship 

Nice to Have 

• Integration with leading cloud AI services or on prem inference stacks  

• Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLM Ops. 

• Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance. 

• Experience implementing tool using agents for API integration and external data access. 

• Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell). 

• Prior work in regulated/secure environments (e.g., ATO, STIGs, Zero Trust) with fast shipping. 

• Familiarity with NVIDIA AI Foundations, OpenAI ChatGPT, and AI assisted dev tools (Cursor, Windsurf, Claude). 

• Contributions to internal frameworks or opensource; mentorship of engineers. 

• Clear communication with engineers, PMs, and security/compliance stakeholders. 

The work: 

Key Responsibilities:

The work: 

• Develop MLOps frameworks and workflows for a variety of domains and applications 

• Deploy, maintain, and optimize ML models and data processes in a production environment  

• Develop custom AI/ML algorithms that translate into mission value 

• Conduct experiments, evaluate model performance, and fine-tune algorithms to improve accuracy and efficiency

• Collaborate with cross-functional teams to integrate AI/ML models into products and services

Here’s what you need: 

• Hands-on experience with scripting languages such as Python, JavaScript or Rust 

• Hands-on experience with developing machine learning models at scale from inception to business impact 

• Hands-on experience with any major deep learning framework and libraries (TensorFlow, PyTorch, HuggingFace) 

• Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC 

• Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP 

• Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 

• Experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment/s

• Experience working with and managing processing of large datasets and computational or analytic jobs  or in academic setting

• Experience monitoring and triaging issues with data processes and tools   

• US Citizenship (No Dual citizenship)

Bonus points if you have:  

• Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required  

Security Clearance: 

• Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

Job Description

This role is to support the hands-on development and support for integrating an Agentic AI system with existing data platforms. The role will focus on data integrations, the query execution layer, as well as implementation of the agentic workflows and orchestration logic.

Responsibilities:

• Develop retrieval pipelines (metadata indexing, embeddings, hybrid search).
• Package the system into deployable services.
• Develops agent tools for structured queries, semantic search, and workflow actions.
• Build ingestion and indexing pipelines into the data lake and vector store.
• Implement APIs, microservices, and backend logic.
• Optimize query routing, caching, and grounding strategies.
• Integrate authentication, authorization, and auditing.
• Support prototyping, demos, and pilot deployments.

Must have:

• 4 years of experience with data & AI
• Experience with Python, APIs/microservices, cloud services
• Understanding of DevOps tools and principals

Nice to have:

• Experience with Model Context Protocol
• Experience with agent frameworks, data engineering, vector databases, SQL engines, distributed systems, RAG
• Bachelors degree

Security Clearance:

• Active TS/SCI with polygraph clearance

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