Sr. MLOps Engineer

Requirements: Strong Python programming. Solid understanding of LLMs, transformers, embeddings, and tokenization. Experience with RAG systems and retrieval optimization. Knowledge of chunking strategies and embedding techniques. Hands-on with vector databases (FAISS, Pinecone, Weaviate, Chroma). Experience with LangChain / LlamaIndex / LangGraph / similar frameworks. API development using FastAPI / Flask. Strong data engineering skills (ETL, preprocessing, unstructured data). Experience evaluating models (precision@k, recall@k, LLM metrics). Familiarity with RAG evaluation tools (RAGAS, TruLens, DeepEval). Experience with cloud platforms (AWS / GCP / Azure). Knowledge of Docker, Kubernetes, CI/CD pipelines. Understanding of data versioning, lineage, and governance. Experience with monitoring, logging, and observability. Exposure to multi-agent systems and orchestration. Knowledge of fine-tuning / LoRA. Awareness of Responsible AI and compliance practices. Job Description: Build and deploy AI applications: copilots, chatbots, code assistants, document agents, and decision automation systems. Design and optimize RAG pipelines (chunking, embeddings, retrieval tuning). Implement multi-agent systems and orchestration workflows. Develop pipelines for prompt data, grounding, and retrieval datasets. Build and maintain document loaders and preprocessing pipelines. Maintain audit trails for model versions, prompts, datasets, embeddings, and outputs. Support CI/CD pipelines for ML code, models, and infrastructure (GitHub‑based). Ensure ML workflows are: Reproducible. Traceable. Auditable (aligned with automotive engineering expectations). Track data lineage across ingestion → transformation → inference. Ensure reproducibility of ML/LLM pipelines. Implement explainability (SHAP, LIME, prompt tracing). Enforce access controls and data security policies. Align with regulatory standards (GDPR, SOC2, Responsible AI. Ensure high data quality (cleaning, parsing, enrichment). Implement data versioning, lineage, and governance for AI systems. Manage embedding lifecycle (indexing, re-embedding, updates). Ensure RAG data freshness via incremental ingestion and re-indexing. Evaluate embedding models and retrieval performance. Measure LLM output quality (relevance, hallucination, faithfulness). Implement robust observability for AI/ML/LLM systems (latency, drift, reliability, performance). Track hallucination rates, response quality, and latency. Monitor token usage, cost, and prompt effectiveness. Build monitoring, observability, and feedback loops. Design and optimize prompt templates and system instructions. Optimize systems for accuracy, latency, and cost.