Senior AI Engineer

Job Description We are looking for a Senior AI Engineer who can design, build, and scale end-to-end AI systems , combining strong foundations in Machine Learning/Deep Learning with hands-on experience in Generative AI (LLMs, RAG, Agentic systems) . This role requires someone who can own problems from idea to production , work closely with leadership, and contribute to building enterprise-grade AI platforms . Key Responsibilities Core AI/ML Engineering (Traditional AI) Design and develop ML/DL models from scratch for real-world problems Handle full lifecycle: data → training → evaluation → deployment → monitoring Work with structured/unstructured data, feature engineering, and model optimization Build scalable ML pipelines with reproducibility and versioning Generative AI & LLM Systems Build and deploy LLM-powered applications (chatbots, copilots, assistants, etc.,) Design and implement RAG pipelines : (Document ingestion, chunking, embeddings, Vector search (FAISS, Pinecone, etc.), Retrieval optimization) Implement prompt engineering, evaluation, and optimization Work with multi-model setups (OpenAI, Claude, open-source LLMs, Bedrock, etc.) Agentic Systems & Advanced Architectures Design and build AI Agents / Agentic workflows (LangGraph, Langsmith/crewAI, etc.) Implement (Tool calling, Planning & reasoning workflows, multi-step decision pipelines) Optimize systems for long-horizon tasks and complex reasoning System Design & Productionization Architect scalable AI systems for enterprise use cases Build APIs and microservices for AI solutions Ensure: (Low latency, High reliability, Cost optimization) Work with Docker, Kubernetes, CI/CD pipelines Observability, Evaluation & Reliability Implement monitoring, logging, and tracing (e.g., Langfuse, Prometheus) Define evaluation frameworks: (Accuracy, Retrieval quality, Hallucination detection) Debug across: (Model, Data, Retrieval, System Layers) Collaboration & Ownership Work directly with AI Lead / CTO / Product teams Translate business problems into AI solutions Own delivery of POCs → production systems Mentor junior engineers and guide best practices Required Skills & Experience Experience 6–8 years of experience in AI/ML and Gen-AI systems with strong exposure to software engineering 4–6 years in traditional ML/DL systems (along with backend systems) 2–3 years in Generative AI / LLM-based systems (LLMs, RAG, agentic workflows) Technical Skills Strong programming: Python (mandatory) ML/DL frameworks: PyTorch / TensorFlow / Scikit-learn LLM frameworks: LangChain / LlamaIndex / LangGraph (preferred) Vector DBs: FAISS / Pinecone / Weaviate / OpenSearch Cloud: AWS / Azure / GCP (Bedrock / Azure OpenAI preferred) APIs: FastAPI / Flask DevOps: Docker, Kubernetes, CI/CD Core Competencies Strong understanding of: ML fundamentals (bias/variance, optimization) NLP and embeddings Retrieval systems Ability to debug end-to-end AI systems Experience in production deployment and scaling Strong system design and problem-solving skills Good to Have Experience with: Multi-agent systems Reinforcement learning Knowledge graphs RAG Experience working with large-scale datasets Exposure to AI safety, governance, and compliance What We Expect Ability to build systems from scratch (not just use APIs) Strong ownership mindset (end-to-end responsibility) Ability to handle evolving requirements Passion to stay updated with latest AI advancements