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-ba…