AI/ML Engineer – Agentic AI

WNS

Required skills

Website: wns.com
Company: https://www.linkedin.com/company/wns-global-services
Seniority: Mid-Senior level
Industries: Software Development
Job details:

Key Responsibilities

Design and develop Agentic AI systems with multi-step reasoning, tool use, and workflow orchestration.
Integrate AI agents into client applications and business workflows.
Build, fine-tune, and evaluate ML/LLM models using established training platforms and pipelines.
Collaborate with clients and engineering teams to gather requirements and translate business problems into AI solutions.
Integrate agentic workflows using APIs, SDKs, and platform-specific tools across web, mobile, and enterprise applications.
Develop RAG pipelines, vector databases, embeddings, and prompt/context engineering strategies.
Define evaluation metrics and conduct model/agent performance experiments.
Optimize AI solutions for scalability, latency, cost, security, observability, and reliability.
Work with data engineering, product, and QA teams to deliver production-ready AI features.
Document architecture, model behavior, integration patterns, and deployment processes.
Stay updated on the Agentic AI and ML ecosystem and recommend relevant frameworks and platforms.

Required Skills & Experience

3–5 years of software engineering experience, including hands-on AI/ML application development.
Experience building Agentic AI systems using LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks.
Experience integrating AI/ML solutions into web, mobile, or enterprise applications using APIs and SDKs.
Knowledge of ML platforms such as AWS SageMaker, Google Vertex AI, Azure ML, or equivalent.
Strong Python programming skills.
Familiarity with JavaScript/TypeScript or mobile/native development is a plus.
Strong understanding of LLMs, prompt engineering, embeddings, RAG, and vector databases.
Experience with Pinecone, FAISS, Weaviate, or similar vector database technologies.
Experience with AWS, Azure, or GCP cloud platforms.
Hands-on experience with Docker and Kubernetes.
Familiarity with MLOps, model versioning, ML CI/CD, monitoring, and observability.
Strong client-facing communication and presentation skills.
Ability to explain technical concepts to non-technical stakeholders.
Experience in IT services/consulting environments is highly preferred.
Ability to manage multiple client engagements and projects simultaneously.

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