AES - Generative AI

Design and develop enterprise-grade Generative AI and Agentic AI solutions.
Build AI-powered assistants, copilots, and intelligent workflow automation platforms.
Implement and optimize: Large Language Models (LLMs), Retrieval Augmented Generation (RAG),
Multi-Agent Systems, Semantic Search architectures
Develop AI orchestration frameworks for autonomous and collaborative AI agents.
Build scalable AI pipelines and microservices using Python and modern AI frameworks.
Design vector search and enterprise knowledge retrieval solutions.
Fine-tune prompts, optimize inference performance, and improve AI response quality.
Develop secure API integrations and event-driven AI architectures.
Collaborate with product, architecture, security, and engineering teams.
Ensure responsible AI governance, security, compliance, and observability standards.
Support deployment, monitoring, and continuous improvement of AI systems in production

At Zensar, we're "experience-led everything". We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.

Part of the $4.8 billion RPG Group, we're a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.

We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

Experience building enterprise AI copilots and autonomous AI agent platforms.
Background in Financial Services, Cards & Payments, Fraud & Disputes, or Customer Servicing
domains.
Experience with conversational AI, intelligent automation, and enterprise search solutions.
Exposure to highly regulated enterprise environments.
Strong stakeholder communication and architecture leadership skills.
Tools & Technologies
AI & GenAI - OpenAI, Azure OpenAI, Anthropic Claude, Hugging Face, LangChain, LangGraph, LlamaIndex,
Transformers
AI/ML & NLP - Python, PyTorch, TensorFlow, Scikit-learn, NLP, Prompt Engineering, AI Agents, RAG
Pipelines
Vector & Search Technologies - Pinecone, Weaviate, FAISS, ChromaDB, Elasticsearch, OpenSearch
Cloud & DevOps - AWS, Azure, GCP, Docker, Kubernetes, Jenkins, GitHub Actions, Azure DevOps
Integration Technologies - REST APIs, Microservices, Kafka, JSON/XML, API Gateway
Monitoring & Observability - Splunk, Dynatrace, Prometheus, Grafan

5+ years of IT experience with strong expertise in AI/ML engineering.
Deep hands-on expertise in: Generative AI & Large Language Models, Retrieval Augmented
Generation (RAG), Multi-Agent Systems & AI Orchestration, Semantic Search & Knowledge Retrieval,
NLP & Machine Learning
Strong programming proficiency in: Python, AI/ML frameworks and libraries
Experience with: LangChain, LangGraph, LlamaIndex, OpenAI/Azure OpenAI APIs, Hugging Face
ecosystem
Strong understanding of: Prompt Engineering, AI Agents, Context Management, Embedding Models,
AI Evaluation Frameworks
Hands-on experience with Vector Databases: Pinecone, Weaviate, ChromaDB, FAISS,
Elasticsearch/OpenSearch
Experience building REST APIs and AI-powered microservices.
Strong knowledge of cloud-native AI deployment patterns on AWS/Azure/GCP.
Experience with Docker, Kubernetes, CI/CD, and MLOps practices.
Familiarity with enterprise-scale secure AI implementation and governance frameworks.