AI Engineer

Job Title – AI Engineer

Company – TCS (MEA)

Location – Dubai

Job type – Full time

About Us:

Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world’s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile™ delivery model, recognized as a benchmark of excellence in software development.

A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world’s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.

Job Description:

Key Accountabilities:

• AI Solution Design & Development:
• Design, build, and deploy AI-powered applications using Azure AI Services, including Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Services (Speech, Vision, Language), Azure AI Search, Azure Functions, and Azure Databricks.
• LLM Integration & Generative AI:
• Develop and integrate Large Language Model (LLM) solutions using Azure OpenAI endpoints (GPT-4.1, GPT-4o, GPT-4o-mini) routed through FAB's centralized AI Hub gateway for governance, observability, and capacity management. Build enterprise use cases such as knowledge search, document intelligence, content summarization, call analytics, sentiment analysis, and workflow automation.
• Python Engineering & Backend Development:
• Write clean, modular, and production-grade Python code for AI/ML model development, API integrations, data processing pipelines, backend services, and automation workflows using frameworks such as LangChain, LlamaIndex, Semantic Kernel, LangGraph, and FastAPI.
• RAG & Semantic Retrieval:
• Implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector search, semantic retrieval architectures, prompt engineering, and evaluation techniques for enterprise knowledge mining and document intelligence.
• Agentic AI Development:
• Design and develop intelligent agentic AI systems capable of planning, reasoning, tool execution, and orchestration across enterprise systems using FAB's GERNAS OS platform and Agent Development Kit (ADK) in a secure and governed manner.
• Model Evaluation & Responsible AI:
• Implement model evaluation metrics for accuracy, hallucination detection, bias, latency, and throughput in alignment with FAB's Responsible AI framework. Ensure AI solutions comply with FAB's security, data privacy, model governance, and regulatory requirements, including controls for accuracy, hallucination, bias, latency, and resilience.
• Deployment & Production Operations:
• Deploy, monitor, and optimize AI solutions in production environments using CI/CD practices, containerization, observability, logging, performance monitoring, and cost optimization techniques.
• Collaboration & Stakeholder Engagement:
• Collaborate with data scientists, platform engineers, DevOps/MLOps engineers, business analysts, and product teams to convert business requirements into scalable AI solutions.
• Innovation & Continuous Learning:
• Continuously evaluate emerging Azure AI, Generative AI, and Agentic AI capabilities and recommend practical adoption opportunities aligned with FAB's AI strategy. Stay abreast of state-of-the-art developments in generative AI and agentic architectures.

TECHNICAL SKILLS: minimum 3-4 yrs of working experience mandatory

Azure AI Services: Demonstrated working experience with Microsoft Azure AI Services including Azure OpenAI, Azure Machine Learning, Azure Cognitive Services, Azure AI Search, Azure Functions, and Azure Databricks.

Python Programming: Strong proficiency in Python programming, including experience with REST APIs, SDKs, asynchronous processing, data manipulation, backend development, and AI application frameworks (LangChain, LlamaIndex, Semantic Kernel, LangGraph, FastAPI).

LLM & Generative AI: Deep understanding of machine learning, statistical modeling, NLP, generative AI principles, LLM application development, prompt engineering, RAG architecture, embeddings, vector databases, semantic search, and model evaluation techniques.

ML Libraries & Frameworks: Advanced proficiency in ML libraries such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and NLP libraries (spaCy, NLTK).

Vector Databases: Experience with vector databases including FAISS, Azure AI Search, ChromaDB, and Pinecone.

DevOps/MLOps: Hands-on experience with DevOps/MLOps practices and tools such as Git, Docker, Kubernetes, CI/CD pipelines, MLflow, Terraform, Azure Monitor, and Application Insights.

Cloud Security & Integration: Understanding of cloud security, identity and access management, data privacy, encryption, logging, monitoring, and secure API integration patterns.

AI Ethics & Governance: Awareness of ethical considerations and responsible AI practices, including fairness, accountability, transparency, bias detection, hallucination mitigation, and compliance in AI systems.

KNOWLEDGE, SKILLS, & EXPERIENCE

Minimum Qualifications:

Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or related field; Master's degree is preferred. Microsoft Certified: Azure AI Engineer Associate (AI-102) certification is highly preferred.

Minimum Experience:

3+ years of hands-on experience in designing, developing, and deploying AI/ML or Generative AI solutions in production environments.

Mandatory hands-on experience with Microsoft Azure AI Services.

Experience working with large-scale datasets and real-time enterprise data.

Experience in financial services, banking, risk, compliance, customer service, or regulated enterprise environments is an advantage.

Knowledge and Skills:

Business Understanding:

Strong understanding of business processes and the ability to identify opportunities for AI-driven optimization and automation across banking domains.

Analytical & Problem-Solving:

Excellent analytical, problem-solving, debugging, and performance optimization skills with the ability to troubleshoot AI applications in production and translate complex business problems into AI solutions.

Communication Skills:

Strong verbal and written communication skills, capable of explaining complex AI concepts to both technical and non-technical stakeholders.

Collaboration:

Proven ability to work effectively in cross-functional teams, collaborating with data scientists, platform engineers, DevOps engineers, business analysts, and product teams.

Responsible AI Mindset:

Understanding of AI governance, model risk, data privacy, security, and compliance requirements in a regulated banking environment.

Production Engineering:

Ability to build, deploy, monitor, and optimize production-grade AI systems with attention to reliability, scalability, cost, and operational excellence.

Adaptability & Learning:

Willingness to stay updated on the latest AI technologies, agentic frameworks, and cloud services, and the ability to apply them to solve evolving business challenges.

Thank you for your interest in applying for this position with TCS. We will review your application and will get back to you if we are considering your interest in this opportunity.

Application Deadline: 30- July -2026

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