Senior AI Engineer / Data Scientist (Agentic AI)

Job Description

Senior AI Engineer / Data Scientist (Agentic AI)

Experience: 8+ Years Location: Office Employment Type: Full-Time / Consultant

Role Overview

We are seeking a highly skilled Senior AI Engineer / Data Scientist with 8+ years of experience in building enterprise-scale AI, Machine Learning, Data Science, and Generative AI solutions. The ideal candidate should possess strong expertise in designing and implementing Agentic AI systems, multi-agent architectures, LLM orchestration frameworks, RAG pipelines, and AI-driven automation solutions.

This role requires a mix of Data Science, AI Engineering, MLOps, Cloud Engineering, and Software Development skills to develop next-generation autonomous AI platforms that deliver measurable business outcomes.

Key Responsibilities

Agentic AI & Generative AI

• Design and develop Agentic AI solutions using autonomous and

multi-agent frameworks.

• Build AI agents capable of reasoning, planning, tool usage, memory

management, and workflow orchestration.

• Implement multi-agent systems for enterprise workflows, analytics,

customer service, and decision intelligence.

• Develop AI copilots, virtual assistants, and autonomous business

agents.

• Design AI orchestration architectures using:

• LangGraph

• LangChain

• AutoGen

• CrewAI

• OpenAI Agent Framework

• Microsoft Copilot Studio

Large Language Models (LLMs)

• Fine-tune and optimize LLMs for enterprise use cases.

• Implement prompt engineering, prompt tuning, and evaluation

frameworks.

• Develop RAG (Retrieval Augmented Generation) architectures.

• Build semantic search and knowledge retrieval solutions.

• Integrate vector databases such as:

• Azure AI Search

• Milvus

Data Science & Machine Learning

• Develop predictive and prescriptive analytics models.

• Build recommendation systems and forecasting solutions.

• Apply advanced statistical analysis and machine learning

techniques.

• Design feature engineering pipelines and model optimization

strategies.

• Build and deploy models using:

• Scikit-Learn

• XGBoost

• TensorFlow

• PyTorch

• Hugging Face

AI Engineering Responsibilities

• Develop scalable AI services and APIs.

• Build enterprise-grade AI microservices.

• Create reusable AI accelerators and frameworks.

• Design AI governance and observability frameworks.

• Implement AI monitoring and model performance tracking.

• Develop AI safety, guardrails, and responsible AI controls.

Cloud & Platform Engineering

Azure (Preferred)

• Azure OpenAI

• Azure AI Search

• Azure Machine Learning

Other Cloud Platforms

• AWS Bedrock

• Amazon SageMaker

• Google Vertex AI

Software Development Skills

Strong hands-on programming expertise in:

• Python (Mandatory)

• SQL

• REST APIs

• GraphQL

Experience with:

• FastAPI

• Microservices Architecture

• Event-Driven Architecture

Required Qualifications

Education

• Bachelor's or Master's degree in:

• Computer Science

• Data Science

• Artificial Intelligence

• Machine Learning

• Engineering

• Related Discipline

Experience

• 8+ years in Data Science, Machine Learning, AI Engineering, or

Software Engineering.

• 3+ years of hands-on experience with Generative AI and LLMs.

• 2+ years of hands-on experience implementing Agentic AI solutions.

• Experience delivering enterprise-scale AI platforms.

Required Technical Skills

Must Have

✅ Agentic AI Frameworks ✅ Generative AI & LLMs ✅ RAG Architecture ✅ Vector Databases ✅ Python Development ✅ Machine Learning & Data Science ✅ Azure AI Services ✅ MLOps & CI/CD ✅ REST APIs ✅ Cloud Architecture

Good to Have

✅ Semantic Kernel ✅ Microsoft Fabric ✅ Databricks ✅ Knowledge Graphs ✅ GraphRAG ✅ Multi-Agent Systems ✅ AI Governance Frameworks ✅ Copilot Studio

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate

• Microsoft Certified: Azure Data Scientist Associate

• Databricks Certified Data Engineer

• AWS Machine Learning Specialty

• Generative AI Certifications (Microsoft/OpenAI)

Success Metrics

• Successful deployment of enterprise AI agents.

• Reduction in manual effort through AI automation.

• Increased model accuracy and business adoption.

• AI platform scalability, performance, and governance compliance.

• Delivery of measurable business value from Agentic AI initiatives.

Target Titles

• Senior AI Engineer

• Lead AI Engineer

• Staff AI Engineer

• Principal AI Engineer

• Senior Data Scientist (Agentic AI)

• AI Solutions Architect