Senior Data Scientist - Agentic AI: 976

C5i; Hyderabad; On-site; INR 3,000,000 - 5,200,000; Full time

Job summary

C5i in Hyderabad, India, seeks an experienced AI engineer to design and deliver agentic AI solutions for enterprise environments. Lead multi-agent workflows, integrate with enterprise systems and enforce governance and responsible AI practices.

Role emphasizes building scalable, secure AI agents, translating business needs into concrete requirements and ensuring reliable operation in real-world workflows.

Qualifications

• 6+ years 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 operationalizing AI or machine learning solutions through Git-based source control, CI/CD, testing, deployment, monitoring and ongoing optimization.
• 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.

Responsibilities

• 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 optimize 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.
• Productionize solutions through source control, automated testing, deployment, release validation, monitoring, issue triage and continuous optimization.

Skills

LLM Engineering

Python

SQL

RAG

Tooling integration

CI/CD

Data Science

MLOps

Responsible AI

Cloud

Tools

Anthropic Claude

AWS Bedrock

Microsoft Copilot

GitHub Copilot

LangChain

Job description

Role focus: 70% Agentic AI and Applied 30% Machine Learning

A Brief Overview:

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 optimize 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.
• Productionize solutions through source control, automated testing, deployment, release validation, monitoring, issue triage and continuous optimization.

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 clear 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.

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 operationalizing AI or machine learning solutions through Git-based source control, CI/CD, testing, deployment, monitoring and ongoing optimization.
• 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.
• Software and Data Engineering: Ability to build production-quality Python and SQL solutions and integrate them with governed enterprise systems.
• LLMOps, MLOps and DevOps: Familiarity with version control, testing, deployment pipelines, observability, monitoring and release management.
• Responsible AI and Security: Ability to design controls for privacy, safety, transparency, data protection, auditability and human oversight.
• Problem Solving, Communication and Ownership: Ability to structure ambiguous problems, make evidence-based trade-offs and lead delivery across functions.

Certifications

Relevant agentic AI, generative AI, AWS, data science, machine learning, MLOps or responsible AI certifications are advantageous but not required.

This position is 100% work from office, 5 days a week.