Job Summary
JD #1 Experience Level: 10+ Years | Classical ML + GenAI+ ML-Ops 1 Position
Overview
An accomplished Principal / Expert Data Scientist with 10+ Year of experience and having deep expertise & hands-on in classical machine learning, GenAI Applications & ML lifecycle,
Key Responsibilities
1. Machine Learning & Statistical Modelling
- Build and optimize complex ML models: regression, classification, clustering, sequence models, time series forecasting.
- Lead sophisticated feature engineering and data quality analysis.
- Apply statistical modelling techniques, experimental design, and Performance evaluation.
- Develop scalable and maintainable ML pipelines for structured and unstructured data.
2. GenAI & LLM Systems
- Architect and develop LLM-based applications using SOTA LLM’s.
- Build RAG pipelines using vector databases (faiss, aisearch, opensearch, PG vector etc).
- Integrate GenAI systems with enterprise apps, APIs, and data sources.
- Model Context Protocol (MCP) & Tooling
- Exposure of Agentic systems and multi-agent workflows
3. Agentic Systems & Model Context Protocol (MCP)
- Exposure to agentic system design, including tool‑calling workflows, planner–executor patterns, and multi‑agent coordination.
- Integrate memory architectures such as episodic, semantic, and vector‑based long‑term memory within agent workflows.
- Implement and manage Model Context Protocol (MCP) servers to enable seamless connectivity between LLMs, tools, APIs, and enterprise applications.
- Collaborate with engineering teams to build reliable, extensible agent tooling and ensure smooth integration into production environments.
4. 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)
5. Leadership & Collaboration
- Lead technical direction for AI solutions.
- Work with product teams to define AI features.
Required Skills & Experience
- 10+ years in Classical ML, GenAI & ML-Ops.
- Strong experience in:
- - Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest
- LangChain, LangGraph, LangSmith (tracing, metrics, evaluations)
- MLflow / Sagemaker / Databricks
- Docker, Git-Ops
- Experience building production-grade GenAI applications.
- Skilled in EDA, DOE, and model evaluation metrics for identifying data patterns, validating hypotheses, and improving model quality
Key Responsibilities
JD #1 Experience Level: 10+ Years | Classical ML + GenAI+ ML-Ops 1 Position
Overview
An accomplished Principal / Expert Data Scientist with 10+ Year of experience and having deep expertise & hands-on in classical machine learning, GenAI Applications & ML lifecycle,
Key Responsibilities
1. Machine Learning & Statistical Modelling
- Build and optimize complex ML models: regression, classification, clustering, sequence models, time series forecasting.
- Lead sophisticated feature engineering and data quality analysis.
- Apply statistical modelling techniques, experimental design, and Performance evaluation.
- Develop scalable and maintainable ML pipelines for structured and unstructured data.
2. GenAI & LLM Systems
- Architect and develop LLM-based applications using SOTA LLM’s.
- Build RAG pipelines using vector databases (faiss, aisearch, opensearch, PG vector etc).
- Integrate GenAI systems with enterprise apps, APIs, and data sources.
- Model Context Protocol (MCP) & Tooling
- Exposure of Agentic systems and multi-agent workflows
3.
Agentic Systems & Model Context Protocol (MCP)
- Exposure to agentic system design, including tool‑calling workflows, planner–executor patterns, and multi‑agent coordination.
- Integrate memory architectures such as episodic, semantic, and vector‑based long‑term memory within agent workflows.
- Implement and manage Model Context Protocol (MCP) servers to enable seamless connectivity between LLMs, tools, APIs, and enterprise applications.
- Collaborate with engineering teams to build reliable, extensible agent tooling and ensure smooth integration into production environments.
4. 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)
5. Leadership & Collaboration
- Lead technical direction for AI solutions.
- Work with product teams to define AI features.
Required Skills & Experience
- 10+ years in Classical ML, GenAI & ML-Ops.
- Strong experience in:
- - Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest
- LangChain, LangGraph, LangSmith (tracing, metrics, evaluations)
- MLflow / Sagemaker / Databricks
- Docker, Git-Ops
- Experience building production-grade GenAI applications.
- Skilled in EDA, DOE, and model evaluation metrics for identifying data patterns, validating hypotheses, and improving model quality
Skill Requirements
JD #1 Experience Level: 10+ Years | Classical ML + GenAI+ ML-Ops 1 Position
Overview
An accomplished Principal / Expert Data Scientist with 10+ Year of experience and having deep expertise & hands-on in classical machine learning, GenAI Applications & ML lifecycle,
Key Responsibilities
1. Machine Learning & Statistical Modelling
- Build and optimize complex ML models: regression, classification, clustering, sequence models, time series forecasting.
- Lead sophisticated feature engineering and data quality analysis.
- Apply statistical modelling techniques, experimental design, and Performance evaluation.
- Develop scalable and maintainable ML pipelines for structured and unstructured data.
2. GenAI & LLM Systems
- Architect and develop LLM-based applications using SOTA LLM’s.
- Build RAG pipelines using vector databases (faiss, aisearch, opensearch, PG vector etc).
- Integrate GenAI systems with enterprise apps, APIs, and data sources.
- Model Context Protocol (MCP) & Tooling
- Exposure of Agentic systems and multi-agent workflows
3. Agentic Systems & Model Context Protocol (MCP)
- Exposure to agentic system design, including tool‑calling workflows, planner–executor patterns, and multi‑agent coordination.
- Integrate memory architectures such as episodic, semantic, and vector‑based long‑term memory within agent workflows.
- Implement and manage Model Context Protocol (MCP) servers to enable seamless connectivity between LLMs, tools, APIs, and enterprise applications.
- Collaborate with engineering teams to build reliable, extensible agent tooling and ensure smooth integration into production environments.
4. 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)
5. Leadership & Collaboration
- Lead technical direction for AI solutions.
- Work with product teams to define AI features.
Required Skills & Experience
- 10+ years in Classical ML, GenAI & ML-Ops.
- Strong experience in:
- - Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest
- LangChain, LangGraph, LangSmith (tracing, metrics, evaluations)
- MLflow / Sagemaker / Databricks
- Docker, Git-Ops
- Experience building production-grade GenAI applications.
- Skilled in EDA, DOE, and model evaluation metrics for identifying data patterns, validating hypotheses, and improving model quality
Must Have Skill
AWS AI/ML (GenAI),Python,Retrieval-Augmented Generation (RAG),Azure AI Services,LLM API Integration,MLOps (Machine Learning Operations)
Positive to Have Skill
Other Requirements
JD #1 Experience Level: 10+ Years | Classical ML + GenAI+ ML-Ops 1 Position
Overview
An accomplished Principal / Expert Data Scientist with 10+ Year of experience and having deep expertise & hands-on in classical machine learning, GenAI Applications & ML lifecycle,
Key Responsibilities
1. Machine Learning & Statistical Modelling
- Build and optimize complex ML models: regression, classification, clustering, sequence models, time series forecasting.
- Lead sophisticated feature engineering and data quality analysis.
- Apply statistical modelling techniques, experimental design, and Performance evaluation.
- Develop scalable and maintainable ML pipelines for structured and unstructured data.
2. GenAI & LLM Systems
- Architect and develop LLM-based applications using SOTA LLM’s.
- Build RAG pipelines using vector databases (faiss, aisearch, opensearch, PG vector etc).
- Integrate GenAI systems with enterprise apps, APIs, and data sources.
- Model Context Protocol (MCP) & Tooling
- Exposure of Agentic systems and multi-agent workflows
3. Agentic Systems & Model Context Protocol (MCP)
- Exposure to agentic system design, including tool‑calling workflows, planner–executor patterns, and multi‑agent coordination.
- Integrate memory architectures such as episodic, semantic, and vector‑based long‑term memory within agent workflows.
- Implement and manage Model Context Protocol (MCP) servers to enable seamless connectivity between LLMs, tools, APIs, and enterprise applications.
- Collaborate with engineering teams to build reliable, extensible agent tooling and ensure smooth integration into production environments.
4. 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)
5. Leadership & Collaboration
- Lead technical direction for AI solutions.
- Work with product teams to define AI features.
Required Skills & Experience
- 10+ years in Classical ML, GenAI & ML-Ops.
- Strong experience in:
- - Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest
- LangChain, LangGraph, LangSmith (tracing, metrics, evaluations)
- MLflow / Sagemaker / Databricks
- Docker, Git-Ops
- Experience building production-grade GenAI applications.
- Skilled in EDA, DOE, and model evaluation metrics for identifying data patterns, validating hypotheses, and improving model quality
📌 Senior Technical Specialist (India)
🏢 HCLTech
📍 India