Data Scientist

Job Title: Data Scientist

Work Location: Bangalore / Bengaluru (Hybrid)

Position Type: Permanent

Interview: Virtual + F2F

Required Experience: 5 years

Need Immediate joiners max 30 days notice

Max Salary: 24LPA

Must have skills: Data Scientist-5+ years, RAG architecture, API integrations, LLM Ops tools, R&D applications

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

• Product & Model Optimization: Analyze existing digital products to improve the performance, reliability, and scalability of current intelligent models.
• LLM Integration: Enhance traditional machine learning (ML) and deep learning (DL) pipelines with LLM features like summarization, Q&A, reasoning, decision support, and copilots.
• RAG Architecture: Design and deploy Retrieval-Augmented Generation (RAG) solutions grounded on enterprise data (documents, manuals, telemetry, tickets, knowledge bases).
• Agentic Workflows: Build autonomous AI workflows with multi-step task planning, function calling for tools/APIs, guardrails, approvals, and contextual memory management.
• Orchestration & Fallbacks: Develop multi-agent collaboration patterns (planner-executor-critic), deterministic workflow engines, and fallback strategies for low-confidence outputs.
• R&D & Patents: Drive innovation through rapid experimentation, patent filings, invention disclosures, and novel solution design.
• Specialized Domains: Implement targeted AI solutions for IoT, robotics, and industrial automation use cases.
• Pipeline Engineering: Construct scalable pipelines for training, evaluation, and deployment across batch and real-time inference environments.
• LLMOps & Governance: Oversee experiment tracking, model registries, and versioning to guarantee reproducibility, traceability, and compliance.
• LLM Evaluation: Define and monitor generative metrics including groundedness, faithfulness, hallucination rate, toxicity, and safety.
• Retrieval Quality: Track vector search performance using metrics like precision, recall, chunking effectiveness, latency, and knowledge coverage.

Required Qualifications

• Education: Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related field (PhD preferred).
• Communication: Exceptional oral and written skills; capable of explaining complex technical concepts to non-technical stakeholders.
• Problem Solving: Proven ability to translate ambiguous business objectives into innovative, flexible solutions.
• Execution: Demonstrated track record of driving change and delivering impactful outcomes in complex environments.

Required Technical Skills

• Core LLMs: Deep expertise in developing scalable, production-grade LLM applications.
• RAG Systems: Hands-on experience designing and deploying end-to-end RAG architectures.
• Data Processing: Experience building ingestion and preprocessing pipelines for unstructured and semi-structured data.
• Chunking Strategies: Expertise in defining chunking methods to maximize retrieval precision and context relevance.
• Vector Mechanics: Strong understanding of embeddings, vector representations, and semantic search algorithms.
• Retrieval Optimization: Experience implementing hybrid search, dense retrieval, and reranking mechanisms to increase response accuracy.
• Explainability: Knowledge of grounding techniques and inline citation strategies for transparent LLM responses.
• Evaluation Frameworks: Hands-on experience establishing test harnesses to measure RAG quality and runtime performance.
• Tool-Using Agents: Experience building autonomous agents utilizing function calling and external API integrations.
• Agent Workflow Design: Proven ability to implement controlled, safe execution patterns for multi-step agent actions.

Preferred / Value-Add Skills

• Vector Databases: Hands-on knowledge of tools like Pinecone, Milvus, Weaviate, Elasticsearch/OpenSearch, Azure AI Search, or FAISS.
• Agent Frameworks: Familiarity with LangChain, LlamaIndex, Semantic Kernel, or deterministic workflow orchestration engines.
• LLMOps Tooling: Exposure to prompt management, version control, observability platforms, A/B testing, and red-teaming.
• Industrial R&D: 3+ years of experience in R&D, supported by published papers, patents, or patent applications.
• Domain Expertise: 3+ years of applied experience in:
• Robotics and automation (including Reinforcement Learning)
• Optimization theory (including black-box optimization)
• Designing IoT algorithms tailored for resource- and power-constrained hardware
• Cloud & DevOps: Experience with AWS, Azure, or GCP, along with containerization (Docker) and Kubernetes orchestration.

Behavioral Competencies

• Ownership: Proactive, self-driven mindset focused on discovering opportunities and taking end-to-end initiative.
• Pragmatism: Strong capacity to balance competing priorities and ship practical solutions efficiently.
• Collaboration: Highly collaborative approach paired with an innovation-first mindset.