WHO YOU ARE

You are a passionate Python and AI/ML Engineer minimum 4 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.

WHAT YOU'LL DO

1. Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
2. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
3. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
4. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
5. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
6. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED 

Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN

Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning

NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch

API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger

Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs

GOOD TO HAVE

Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure

TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js

MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD

Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes

WHO YOU ARE

You are a passionate Python and AI/ML Engineer minimum 4 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.

WHAT YOU'LL DO

1. Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
2. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
3. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
4. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
5. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
6. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED 

Python
Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
NumPy · Pandas · FastAPI · Scikit-learn
Machine Learning
Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
PyTorch · TensorFlow · XGBoost · DBSCAN

Generative AI (2+ yrs)
Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
LLMs · RAG · Prompt Eng. · Fine-tuning

NLP & Search / Ranking
Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
spaCy · BERT · FAISS · Elasticsearch

API Development
Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
REST · FastAPI · OAuth2 · Swagger

Databases
Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
PostgreSQL · MongoDB · Vector DBs

GOOD TO HAVE

Cloud Platforms
Deploys and scales AI workloads on AWS, Azure, or GCP.
AWS · Azure

TypeScript / JavaScript
Frontend or full-stack exposure for building ML-powered product interfaces.
TypeScript · React · Node.js

MLOps
Manages the ML lifecycle — tracking, versioning, and pipeline automation.
MLflow · Kubeflow · CI/CD

Containerization & Orchestration
Packages and scales AI services using containers and cluster management.
Docker · Kubernetes

BOLD is seeking for the newest addition to our existing Data Science team, you’ll combine your analytical skills and business knowledge to deliver key insights to our Product and Management teams. You’ll help drive product development using our vast user data sets to enhance our current products and to help us find inferences and relationships in this data to build new products – More towards the role required

ABOUT THIS TEAM

The Data Science department at BOLD is responsible for discovering patterns and trends in datasets to get insights, creating predictive algorithms and data models, improving the quality of data or product offerings by utilizing machine learning techniques, distributing suggestions to other teams and top management, and using data tools such as Python and SQL.

The Data Science team actively collaborates with other vertical teams such as Engineering, Portals, BI, Product, Legal. Most of the projects are focused around problems that require a mix of natural language processing and machine learning. Some of the active projects are resume parsing, ranking, summary generation, data quality and scoring, content generation, job recommendations, and conversion analysis. Apart from the business initiatives, the team also explores state of the art methods and keeps them upto to date with technology.

WHAT YOU’LL DO 

• Build AIML models that serve for content generation, content recommendation, and content moderation using LLMs. 
• Responsible for converting business requirements into robust data science algorithms. 
• Responsible for providing technical leadership to a team of data science engineers. 
• Responsible for design, development, deployment of AI/ML, NLP, statistical & content recommendation algorithms that may not require LLMs.  
• Leverage our rich data sets of user data to perform research, develop models and create data products with our Development & Product teams 
• Develop novel and scalable data systems in cooperation with our system architects that leverage datasets using machine learning techniques to enhance the user experience 
• Collaborates effectively with cross functional teams to deliver end-to-end products & features. 
• Demonstrates ability to multi-task and re-prioritize responsibilities based on changing requirements. 
• Responsible for creating model validation framework. 
• Leading a data science team that delivers actionable results through the combination of data science solutions, business skills, statistical knowledge, and understanding of data. 
• Distilling complex analytical results into presentable, digestible, and actionable feedback for product and engineering teams. 
• Recruit, build and retain a top-tier data science team that brings together top talent at all levels, cutting edge technology, and analytic innovation. 
• Assess, inspire and motivate a high performing team of data science professionals. 

WHAT YOU’LL NEED

• Proven experience in solving open-ended business problems using data-science/ML/AI from initial ideation stage up to delivering the ready-to-use solution. 
• Strong Programming experience in Python. Familiar with various Machine Learning algorithms and Statistical methods. 
• 10+ yrs of working experience in MLData science with NLP background  
• Experience with frameworks that optimizes prompt engineering (langChain/crewai/etc) 
• Must have delivered multiple data science product(s) in production. Deployment with LLMs is a plus. 
• Practical LLM/RAG experience for search quality  such as  query understanding, semantic retrieval, reranker design .  
• Experience with Solr/OpenSearch/Elasticsearch … 
• Experience with embedding & vector search  
• Experience designing and implementing ML systems with Data Pipelines (Data Cleaning and creative Feature Engineering), Model Iterations and Evaluation, High throughput ML(Model Training, Evaluation, GPU vs CPU, XGBoost ML), Python ML Libs(TensorFlow, PyTorch). 
• Experience in leading a team of 3-5 data scientists. 
• Experience using tools to deploy models into the production environment such as Jenkins 

 EXPERIENCE- 

• Engineering Manager, Data Science- 10 years+

#LI-SV1

Key Responsibilities

As a AI Engineer/Senior AI Engineer and a key member of our engineering team in India, you will be responsible for designing, building, and owning the end-to-end lifecycle and release of our most critical AI-powered features within a microservices architecture. This role requires a balance of advanced technical depth in AI/ML and a strong sense of product ownership, ensuring our solutions deliver tangible user value at scale.

1. End-to-End AI System Ownership

• Design & Development: Design, build, and deploy production-grade AI systems, ranging from traditional Machine Learning models to modern LLM-powered features.
• Architecture & Deployment: Lead AI projects from concept to execution, including designing robust microservice architectures, modeling, rigorous evaluation, and secure deployment on AWS.
• Data Expertise: Work extensively with unstructured data, chunking, embeddings, vector databases (e.g., Pinecone, pgvector), and address complex real-world data quality challenges.
• Production Standards: Own the release process end-to-end, applying high engineering standards to ensure scalability, operability, and maintainability, aligning with Enboarder’s microservices philosophy.

1. Generative AI & LLM Focus

• LLM Integration: Evaluate, integrate, and operationalize LLM-based tools and APIs, including but not limited to Anthropic Claude and AWS Bedrock, to enhance product capabilities.
• Advanced Techniques: Apply expertise in prompt engineering, Retrieval-Augmented Generation (RAG), and advanced evaluation metrics for generative outputs.
• Agentic Systems: Explore and implement agentic AI frameworks, workflow orchestration (e.g., Langgraph, CrewAI, StrandsAgents), or multi-agent systems to automate complex user journeys.

1. Collaboration, Leadership, and Strategy

• Product Collaboration: Collaborate closely with product, design, and other engineering teams to translate business requirements into high-impact AI capabilities that drive measurable user and business outcomes.
• Technical Leadership: Stay updated on the latest AI/ML advancements, contribute to the company’s long-term technical strategy, and evaluate new technologies for adoption.
• Mentorship: Actively mentor and guide junior AI engineers, fostering a culture of technical excellence, clean code practices, and continuous experimentation.

Required Skills & Experience

• Total Experience: 6+ years of professional experience in the IT/Technology industry, with a substantial focus on building and deploying production systems.
• AI/ML Experience: 4+ years of direct experience in building and deploying ML or LLM-based systems in production environments, ideally within a high-traffic SaaS environment.
• Programming Mastery: Strong proficiency in Python and modern AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn).
• Generative AI Tooling: Hands-on experience with LLM-based frameworks such as LangChain, AWS Bedrock, or comparable in-house/open-source solutions.
• Vector Databases: Practical experience working with vector databases (e.g., FAISS, Pinecone, PGVector) for scalable similarity search and RAG implementation.
• Cloud Architecture: Extensive experience deploying, monitoring, and operating applications on AWS, demonstrating deep awareness of scalability, security, and cost-optimization in a microservices environment.

Preferred Qualifications (Nice to Have)

• Familiarity with FastAPI and containerization technologies (Docker, Kubernetes).
• Experience with MLOps tools such as MLflow, Airflow, or similar workflow orchestration platforms.
• Ability to balance technical trade-offs between model performance, inference cost, and system complexity in AI deployments.
• Direct experience with Fine Tuning or Pre-training large language models (LLMs) on domain-specific data.

*NOTE:* Hybrid work with 3 days in office.