Data Scientist

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 RetrievalAugmented 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

• Masters 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 RetrievalAugmented 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.

Required Skills:

Data Science