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).

· Solid 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 protected 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

📌 Data Scientist (India)
🏢 TalentNest Solutions
📍 India