Key Responsibilities
· Analyze existing digital products to understand current intelligent models and improve their performance, reliability, and scalability.
· Enhance traditional ML and DL pipelines by incorporating LLM-based capabilities such as summarization, Q&A, reasoning, decision support, and copilots.
· Design and implement LLM-based solutions using Retrieval-Augmented Generation (RAG) to ground responses on enterprise data including documents, manuals, telemetry, tickets, and knowledge bases.
· Build Agentic AI workflows that enable multi-step task planning, tool and API invocation through function calling, controlled action execution with guardrails and approvals, and contextual memory management.
· Develop agent orchestration patterns such as multi-agent collaboration (planner–executor–critic), deterministic workflow engines, and fallback mechanisms for low-confidence retrieval or reasoning.
· Drive innovation through experimentation and contribute to invention disclosures, patents, and novel solution approaches.
· Design and implement AI solutions for IoT, robotics, and automation use cases.
· Build and maintain scalable pipelines for model training, evaluation, and deployment across batch and real-time inference scenarios.
· Manage experiment tracking, model versioning, and model registries to ensure reproducibility, traceability, and governance.
· Define and track LLM-specific evaluation metrics, including groundedness, faithfulness, hallucination rate, toxicity, and safety.
· Monitor retrieval system quality using metrics such as precision, recall, chunking effectiveness, latency, and knowledge coverage.
Required Qualifications
· Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related field (PhD preferred).
· Strong oral and written communication skills; ability to explain technical concepts to non-technical stakeholders.
· Demonstrated ability to take ambiguous objectives and design innovative, flexible solutions.
· Proven track record of delivering impactful outcomes and driving change in complex environments.
Required Technical Skills
· Strong expertise in Large Language Models (LLMs) and building scalable, production-grade applications using them.
· Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
· Experience building document ingestion and preprocessing pipelines for unstructured and semi-structured data.
· Expertise in defining effective chunking strategies to optimize retrieval quality and context relevance.
· Strong understanding of embeddings, vector representations, and vector search techniques.
· Experience implementing retrieval and reranking mechanisms to improve response accuracy.
· Familiarity with grounding and citation strategies to ensure reliable and explainable LLM outputs.
· Hands-on experience establishing evaluation frameworks to measure RAG quality and performance.
· Experience building tool-using agents leveraging function calling and API integrations.
· Proven ability to design and implement multi-step agent workflows with safe and controlled execution patterns.
Preferred / Nice-to-Have Skills (Strong Value Add)
· Experience with vector databases and search platforms (e.g., Pinecone, Milvus, Weaviate, Elasticsearch/OpenSearch vector, Azure AI Search, FAISS).
· Familiarity with agent frameworks/orchestration (e.g., LangChain, Semantic Kernel, LlamaIndex) and workflow engines for controlled execution.
· Experience with LLMOps tooling: prompt/version management, evaluation harnesses, observability, A/B testing, red teaming.
· 3+ years of industrial R&D with publications/patents/patent applications.
· 3+ years experience in:
· robotics/automation (including reinforcement learning),
· optimization theory (including black-box optimization),
· designing IoT algorithms under resource/power constraints.
· Cloud experience (Azure/AWS/GCP), containerization (Docker), and scalable deployment patterns (Kubernetes).
Behavioral Competencies
· Strong ownership mindset; proactive in identifying new opportunities and leading initiatives.
· Ability to reconcile competing priorities and deliver pragmatic solutions.
· Collaborative team player with an innovation-first approach.
Pay: ₹605,283.23 - ₹1,982,089.73 per year
Work Location: In person
