Gen AI Engineer
Fractal Analytics3 days ago
Pune, India +3 moreSenior
Responsibilities
- Design, develop, and implement production-grade generative AI and LLM solutions.
- Build APIs, pipelines, orchestration layers, RAG architectures, agentic workflows, and reusable AI frameworks.
- Define architecture patterns and make trade-offs across latency, cost, accuracy, reliability, safety, and maintainability.
- Create evaluation frameworks, monitoring, guardrails, fallback strategies, and observability for LLM systems.
- Develop reusable accelerators, templates, reference architectures, internal intellectual property, and maintainable code libraries.
- Contribute across the software development lifecycle, including design, implementation, testing, deployment, maintenance, code reviews, and design reviews.
- Translate ambiguous business problems into structured AI approaches, measurable outcomes, and solution success metrics.
- Engage with business and technical stakeholders, present solutions, explain architectural trade-offs, and influence decision-making.
- Contribute to client discussions, proofs of concept, solution prototyping, and proposal development.
- Drive responsible AI practices, including hallucination mitigation, bias handling, governance, and identifying when alternative approaches are preferable to LLMs.
- Mentor junior team members and contribute to internal training, knowledge sharing, quality standards, and capability building.
- Support ad hoc and non-generative AI/ML initiatives such as computer vision, document digitization, and data structuring.
Requirements
- Experience designing and implementing advanced generative AI and NLP solutions using LLMs, RAG, prompt engineering, agents, and multimodal models.
- Experience with LangChain, LlamaIndex, vector databases, Azure OpenAI function calling, and at least one agentic framework such as CrewAI, AutoGen, LangGraph, n8n, LangFlow, SmolAgents, or Semantic Kernel.
- Knowledge of advanced RAG approaches, including hybrid retrieval, knowledge graph retrieval, multi-hop RAG, hierarchical and contextual retrieval, and RAG evaluation and monitoring.
- Knowledge of classical NLP applications including text classification, topic modeling, question answering, conversational AI, search, Document AI, summarization, content generation, and named entity recognition.
- Experience with Databricks Genie, Databricks AI/BI, Azure Databricks, MLflow model lifecycle management, Unity Catalog, and Delta Lake.
- Proficiency with Python and FastAPI; Flask and Docker are good-to-have skills.
- Knowledge of Azure services and tools including Azure DevOps, Azure Boards, Azure Function Apps, Azure Blob Storage, Azure Cognitive Services, Azure AI Search, and Microsoft Copilot Studio.
- Experience or familiarity with MCP servers, tools, skills, and reusable component creation and maintenance.
- Good-to-have experience with AgentOps/LLMOps, agent monitoring, evaluation and debugging frameworks, LLM observability and tracing, prompt/version management, experimentation, governance, compliance, cost optimization, and Azure DevOps CI/CD pipelines.
- Familiarity with LangSmith, LangFuse, Weights & Biases, Azure Document Intelligence or equivalent, document digitization, and OCR methods.
- Strong analytical, problem-solving, communication, accountability, curiosity, flexibility, and willingness to understand business context.
- Ability to collaborate with technical and non-technical stakeholders, drive work to completion, and contribute to proposals, proofs of concept, and business initiatives.
Categories
About Fractal Analytics
Fractal builds enterprise AI and analytics solutions—delivered as consulting services and software products—for large global enterprises, especially Fortune 500 firms. Its teams embed AI in decisions across marketing, pricing, supply chain, forecasting, and customer experience. Founded in 2000 and headquartered in New York, Fractal is publicly listed and operates across North America, EMEA, and APAC.