> Markdown version of [/jobs/ext/4026469-senior-data-scientist](https://www.wearedevelopers.com/jobs/ext/4026469-senior-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Data Scientist

- **Company:** HCL Technologies
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Cloud Computing, Software Documentation, Software Debugging, Github, Python (Programming Language), Machine Learning, Open Source Technology, Parsing, Regression Testing, Markdown, Software Engineering, Systems Architecture, Unstructured Data, Software Repository, Cloud Platform System, Feature Engineering, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Model Validation, Generative AI, Agentic-AI, Git, Build Management, LangSmith, Build Tools, FAISS, Drift Detection, Model Context Protocol, Software Version Control, Databricks
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-data-scientist-palo-alto-ca--758dfcda-7b40-4f2e-bc45-4c0c79d24932

## About the Role

Data Science, Ecosystems, Software Development, Memory Hardware, Shallow Parsing, Regression Testing, Quality Monitoring, System Architecture, Communication Systems, Agent Communication, This role demand strong GenAI experience, emerging mastery in Agentic AI Systems, and a good foundation in classical ML.

You will design and build intelligent, tool-using agents, multi-agent systems, RAG pipelines, and LLM-based applications leveraging the LangChain , LangGraph ecosystem, LangSmith for evaluation.

Key Responsibilities

This role demand strong GenAI experience, emerging mastery in Agentic AI Systems, and a good foundation in classical ML.

You will design and build intelligent, tool-using agents, multi-agent systems, RAG pipelines, and LLM-based applications leveraging the LangChain , LangGraph ecosystem, LangSmith for evaluation., * 5-10 years total experience, with 2-4+ years hands-on GenAI.

 * Hands-on expertise with:
 * LangChain, LangGraph
 * LangSmith (tracing, metrics, evaluations)
 * MCP tooling and agent tool integration
 * ReAct, Tree of Thoughts, multi-agent orchestration
 * RAG patterns and vector databases
 * Strong coding expertise in Python.
 * Classical ML foundations (tree models, regression, etc.).
 * Experience working with LLM APIs and/or open-source LLMs.
 * Experience building and debugging production-quality GenAI pipelines.
 * Aws/azure
 * GIT Ops
 * Prior experience building complex multi-agent systems for real-world applications.
 * Knowledge of multi-modal LLMs (vision, speech, code).
 * Familiarity with structured evaluation of LLM systems (hallucination tests, safety assessments etc ).
 * Experience in enterprise-grade LLM deployments.

Other Requirements

BE or Equivalent degree

## Description

* LangChain, LangGraph
 * Implement RAG architectures with:
 * Retrieval, reranking, chunking, memory strategies
 * Vector DBs (faiss, aisearch, opensearch, PG vector etc).
 * Design prompt-engineering strategies:
 * Instruction-following
 * ReAct (Reasoning + Acting)
 * Chain-of-thought structuring
 * Self-reflection and planning loops
 * Evaluation Strategy
*

 * Implement evaluation frameworks for Classical ML and GenAI systems, covering statistical validation, reliability, and robustness.
 * Assess LLM outputs, RAG pipelines, and agent workflows for grounding quality, relevance, and retrieval accuracy (e.g., recall@k, precision@k).
 * Use LangSmith for tracing, automated evaluations, regression testing, and continuous system-level quality monitoring

 2. Agentic System Architecture

 * Build agentic workflows:
 * Tool-calling agents
 * Planner-executor systems
 * Multi-agent communication systems
 * Hierarchical agent architectures
 * Deep Agents
 * Integrate memory systems:
 * episodic memory
 * semantic memory
 * vector-based long-term knowledge
 * Implement evaluation frameworks for agentic systems using LangSmith.

 3. Model Context Protocol (MCP) & Tooling

 * Implement MCP servers for external tool connectivity.
 * Build tools that allow agents to interact with:
 * APIs
 * Code execution environments
 * Knowledge bases
 * Company applications

 4. Classical ML (Foundational DS Skills)

 * Apply ML models to structured/unstructured data.
 * Conduct feature engineering, model selection, hyperparameter tuning.
 * Build interpretable models where required.

 5. Engineering & Integration

 * Collaborate with backend engineering teams to seamlessly integrate agentic and GenAI systems into production applications.
 * Implement observability, tracing, and monitoring for GenAI workflows using LangSmith to ensure reliability and system-level transparency.

 6. Cloud ML-Ops & Quality

 * ML Modelling, data drift, concept drift, model quality monitoring.
 * Hands-on experience across AWS/ Azure/ Databricks, with flexibility to work on any cloud platform.
 * Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)