Senior Technical Specialist

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

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