Job Description

Note: We Looking only immediate Joiner

Role: Data Scientist

Experience:5 years+

Location: Bangalore

Job Description:

As a Data Scientist, you will identify business trends and solve complex problems using large-scale data and advanced AI techniques. You will design, develop, and deploy high-impact solutions ranging from classical ML/DL to LLM-powered applications including RAG-based architectures and Agentic AI systems (tool-using, workflow-driven AI that can plan, reason, and act). You will collaborate with stakeholders and cross-functional teams to drive innovation, improve existing intelligent products, and deliver measurable outcomes.

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.