Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

• Strong experience creating and deploying cloud infrastructure on Azure, AWS, and GCP
• Understanding of Python and Docker for developing web APIs
• Understanding of deploying solutions to Kubernetes.
• Familiarity with serverless technologies across functions, queues, file storage, API management, and key‑value stores
• Experience with integration patterns and working with various APIs
• Knowledge of DevOps principles and Git, preferably with experience in modern techniques such as monorepos
• Proficiency in Infrastructure‑as‑Code languages such as Terraform and CloudFormation
• Understanding of design patterns for building scalable and efficient systems
• A quality‑driven, test‑first mindset
• Excellent written and verbal communication skills
• Familiarity with Diagrams‑as‑Code for documenting infrastructure architecture is a plus
• Understanding of modern authentication protocols such as OAuth2 and OIDC
• Consideration of cross‑cutting concerns like observability and security in infrastructure design
• Contributions to open‑source projects are a plus

We’re ideally looking for someone who is comfortable across the entire stack and can take ownership of feature delivery from design to deployment. The ideal candidate should have:

• 5+ years of experience with Python and FastAPI (async programming)
• Experience with Redis caching patterns and cache invalidation strategies
• Experience with event-driven architectures (Pub/Sub, SNS/SQS, or similar message brokers)
• Familiarity with SQLAlchemy ORM and relational database patterns
• Experience writing comprehensive test suites (pytest, integration testing)
• Comfortable with REST API design and microservices communication patterns
• Experience with AI-assisted development tools (Cursor, Copilot, Claude Code)
• Experience with Git and CI/CD pipelines

Nice to Have

• Experience with Strapi CMS or similar headless CMS platforms
• Experience with both GCP and AWS cloud services
• Familiarity with i18n patterns (locale handling, translation key management)
• Experience in Java and typescript

Job Overview:

We are seeking an experienced Senior Data Engineer to build and optimize scalable data platforms using Microsoft Fabric, Databricks (and/or Snowflake). The role focuses on designing reliable data pipelines, lakehouse and warehouse models, and semantic layers that enable enterprise analytics, BI, and AI/Gen AI use cases.

You will work closely with analytics, BI, and data science teams to deliver high-quality, performant, and governed data solutions, while driving best practices in data engineering, optimization, and platform design.

Key Responsibilities

• Design, build, and maintain end-to-end data solutions on Microsoft Fabric, Databricks, including Pipelines, Notebooks, Lakehouse, Data Warehouse, and Semantic Models.
• Implement scalable data ingestion, transformation, and loading (ETL/ELT) using Fabric Pipelines and PySpark.
• Develop robust data models and schemas optimized for analytics, reporting, and AI-driven consumption.
• Create and maintain semantic models to support Power BI and enterprise BI solutions.
• Engineer high-performance data solutions that meet requirements for throughput, scalability, quality, and security.
• Author efficient PySpark and SQL code for large-scale data transformation, data quality management, and business rule processing.
• Build reusable framework components for metadata-driven pipelines and automation.
• Optimize Lakehouse and Data Warehouse performance, including partitioning, indexing, Delta optimization, and compute tuning.
• Develop and maintain stored procedures and advanced SQL logic for operational workloads.
• Design and prepare feature-ready datasets for AI and GenAI applications.
• Collaborate with data scientists and ML engineers to productionize AI pipelines.
• Implement data governance and metadata practices required for responsible AI.
• Leverage Fabric, Databricks capabilities to orchestrate and monitor AI-related data workflows.
• Apply data governance, privacy, and security standards across all engineered assets.
• Implement monitoring, alerting, and observability best practices for pipelines and compute workloads.
• Drive data quality initiatives, including validation frameworks, profiling, and anomaly detection.
• Partner with analytics, BI, data science, and product teams to understand requirements and translate them into technical solutions.
• Mentor junior engineers and contribute to engineering standards and patterns.
• Participate in architecture reviews, technical design sessions, and roadmap planning.
• Develop and deliver dashboards and reports using Power BI and Tableau.

Required Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 7+ years of professional experience in data engineering or a related discipline.
• Mandatory hands-on expertise with Microsoft Fabric, Databricks or Snowflake including:
  • Pipelines
  • Notebooks
  • Lakehouse
  •  Data Warehouse
  • Semantic Models
• Advanced proficiency in PySpark for distributed data processing.
• Strong command of SQL for analytical and operational workloads.
• Experience developing and optimizing stored procedures and complex SQL transformations.
• Understanding of GenAI architectures, vectorization, embedding pipelines, and data preparation for LLM use cases.
• Strong knowledge of data modeling, ETL/ELT patterns, and modern data lakehouse principles.
• Experience designing and optimizing large-scale data pipelines in cloud environments (Azure preferred).
• Excellent problem-solving skills with the ability to analyze complex data workflows.
• Proficiency in creating interactive dashboards and reports using Power BI and Tableau.