Job Summary

This is a high-impact, execution-focused role responsible for building and delivering AI-powered agents and agentic workflows that address critical business challenges. The role combines strong applied data science foundations with hands-on delivery using commonly used technology ecosystems, including Anthropic Claude on AWS Bedrock, Claude Code, Microsoft Copilot, GitHub Copilot and related frameworks and observability tools.

You will partner with Data & Analytics leaders and cross-functional teams to design and deliver scalable, secure, and compliant AI solutions in enterprise environments. This role is ideal for someone who excels at translating business needs into clear requirements and delivering high-quality AI agents that operate reliably within real-world workflows.

Key Responsibilities

Agentic AI Solution Design and Delivery

- Lead the design, development and testing of single-agent and multi-agent solutions using LLMs, prompt and context engineering, retrieval-augmented generation (RAG), tool use and structured outputs.
- Translate business problems into agent goals, workflow logic, system interactions, acceptance criteria, exception handling and human-in-the-loop controls.
- Integrate agents with enterprise platforms, APIs, MCP servers, databases, SaaS applications and governed data sources to enable secure end-to-end workflow execution.
- Build and optimise retrieval, grounding, memory and orchestration patterns that improve accuracy, traceability and task completion.
- Establish evaluation and observability practices covering quality, groundedness, safety, latency, cost, failure modes and regression testing.
- Productionise solutions through source control, automated testing, deployment, release validation, monitoring, issue triage and continuous optimisation.

Applied Data Science and Machine Learning

- Frame business problems as analytical, statistical, machine learning or agentic AI use cases, selecting the simplest effective approach.
- Prepare structured and unstructured data and perform feature engineering, model development,



validation and error analysis using reproducible Python and SQL workflows.
- Design experiments and measurement approaches to demonstrate model performance, incremental value and business impact.

Collaboration, Governance and Leadership

- Partner with Product, Engineering, Security, Legal, Compliance and business teams to define priorities, dependencies, controls and acceptance criteria.
- Embed responsible AI practices, including access controls, privacy protections, guardrails, human oversight, fallback mechanisms and audit-ready logging.
- Drive strong engineering and analytical standards through modular design, code quality, peer review, testing discipline and transparent documentation.
- Communicate implementation choices, analytical findings, limitations, delivery risks and production readiness to technical and non-technical stakeholders.
- Provide technical guidance and contribute reusable patterns, evaluation assets and delivery standards across Data & Analytics.

Required Qualifications

- Bachelor s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative field. A master s degree is preferred.

Must-Have Experience

- 6+ years of experience in data science, machine learning, applied AI or software engineering, including delivery of production solutions.
- Hands-on experience building LLM-powered applications or agents using prompt and context engineering, RAG, tool or function calling, orchestration and structured evaluation.
- Experience implementing multi-step workflows with enterprise integrations, exception handling, state management and human-in-the-loop activities.
- Strong Python and SQL skills, with experience writing maintainable,



testable and reusable code.
- Experience integrating applications with APIs, databases, vector stores, cloud services and enterprise data sources.
- Experience operationalising AI or machine learning solutions through Git-based source control, CI/CD, testing, deployment, monitoring and ongoing optimisation.
- Working knowledge of statistical modelling, machine learning, experimental design and model validation.
- Ability to deliver complex solutions with limited oversight and collaborate effectively across technical and business teams.

Preferred Experience

- Experience with Anthropic Claude and AWS Bedrock, including model access, knowledge bases, agents, guardrails and enterprise integrations.
- Experience building enterprise agents with Microsoft Copilot or Copilot Studio and connecting them to Microsoft 365, Power Platform or business applications.
- Experience using Claude Code and GitHub Copilot to accelerate software development while maintaining code quality and engineering controls.
- Experience with LangChain, LangGraph or similar orchestration frameworks, and Langfuse or comparable evaluation and observability tooling.
- Experience with vector databases, semantic search, document processing, multi-agent patterns and adversarial or red-team testing.
- Experience applying predictive modelling, natural language processing, optimisation, time-series forecasting or recommendation methods.

Skills and Abilities

- Agentic AI and LLM Engineering: Strong understanding of RAG, tool use, orchestration, memory, structured outputs, context engineering and agent evaluation.
- Data Science and Statistics: Strong grounding in data exploration, feature engineering, statistical reasoning, machine learning, experimentation and validation.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr Data Scientist (Hyderabad)
🏢 Trinet Group
📍 Hyderabad