Senior AI Engineer- Remote, India

Responsibilities

• Design and build production LLM applications: RAG pipelines, agentic and tool-calling systems, and multi-step reasoning workflows.
• Engineer retrieval layers on vector databases, covering chunking strategy, embedding model selection, metadata filtering, hybrid search and re-ranking.
• Design context and prompting strategies: system prompt architecture, structured output, function/tool schemas, context compression and memory management.
• Build evaluation harnesses and guardrails to measure accuracy, groundedness, safety, regression and hallucination rates, and drive improvements from the results.
• Optimize inference cost and latency through caching, batching, model routing, quantization, streaming and speculative decoding.
• Deploy and serve models using vLLM, TGI, Triton or managed inference, including self-hosted open-weight deployments for data-sensitive clients.
• Select and integrate foundation models (OpenAI, Anthropic, Google, Llama, Mistral, Qwen) against accuracy, latency, cost and data-residency requirements.
• Expose AI capabilities as secure, well-documented, streaming-capable APIs and integrate them into enterprise applications.
• Operate AI services in production with containers and CI/CD, including tracing, token and cost telemetry, prompt and model versioning, and rollback.
• Lead code and design reviews on AI components, mentor engineers, and contribute reusable frameworks and standards to the AI practice.
• Prototype against new model releases, agent frameworks and inference techniques, and bring what proves out into client delivery.

Essential Skills

Job

• 4-6 years of engineering experience, with at least 2-3 years building and shipping production LLM or Generative AI systems.
• Expert Python, covering asynchronous programming, typing, testing, streaming and API development with FastAPI.
• Deep hands-on LLM application development: prompt and context engineering, structured output, tool calling, function schemas and failure handling.
• Working understanding of transformer internals: tokenization, attention and context-length costs, embeddings, temperature and sampling, and decoding behaviour.
• Proven experience designing and shipping RAG systems, including chunking strategies, embedding selection, hybrid retrieval, re-ranking and retrieval evaluation.
• Hands-on experience with agent and orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI or the OpenAI/Anthropic agent SDKs.
• Strong working knowledge of vector databases (e.g., Pinecone, Weaviate, Qdrant, FAISS, pgvector) and index and similarity trade-offs.
• Practical experience with fine-tuning and model adaptation (LoRA/QLoRA, PEFT, instruction tuning), including when not to fine-tune.
• Hands-on PyTorch, Hugging Face Transformers and the surrounding ecosystem (datasets, accelerate, PEFT).
• Experience with LLM evaluation and observability tooling (RAGAS, LangSmith, DeepEval, Langfuse) and building custom eval sets.
• Experience reducing inference cost and latency in production, with a clear account of what you measured and what you changed.
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP) and its AI/ML services, plus Docker and CI/CD.
• Strong database and API fundamentals across SQL (PostgreSQL) and NoSQL (MongoDB, Redis), with Git and disciplined code review practices.

Personal

• Strong engineering judgement, with the ability to reason about accuracy, cost and latency trade-offs rather than defaulting to the largest model.
• Genuine depth of interest in the field: reads papers, model cards and evaluations, and tests claims instead of taking them at face value.
• Clear communication skills, including the ability to explain AI trade-offs and limitations to non-technical stakeholders.
• Strong ownership and accountability, with the ability to drive work independently end to end.
• Able to mentor engineers and lift the technical standard of the team.
• Comfortable working in fast-paced, ambiguous and rapidly evolving problem spaces.

Preferred Skills

Job

• Experience with multi-agent orchestration, planning loops and long-running autonomous workflows.
• Experience with multimodal models covering vision, speech or document understanding, including OCR-heavy document pipelines.
• Experience with distributed or accelerated training and GPU resource management.
• Knowledge of graph-based retrieval (GraphRAG), knowledge graphs and hybrid symbolic approaches.
• Experience with model serving infrastructure, autoscaling GPU workloads and cost governance.
• Familiarity with responsible AI practices: bias evaluation, red-teaming, PII handling and governance frameworks (EU AI Act, NIST AI RMF).
• Exposure to microservices architecture, event-driven systems and distributed systems fundamentals.
• Open-source contributions, published work, or a portfolio of AI systems built outside of client work.

Personal

• Builder's mindset, comfortable prototyping quickly and discarding what does not hold up.
• Consulting orientation, balancing technical ideals against client timelines and budgets.

Other Relevant Information

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Relevant certifications in AI/ML or cloud platforms (AWS/Azure/GCP) are a plus.
• A portfolio of production Generative AI systems, open-source contributions or published work is highly desirable.
• This role offers the flexibility of working remotely in India.