AI Engineer - Contract

AI Engineer – Assistant Manager
Role Level: Assistant Manager (AM)
Practice: AI & Data
Focus Areas: LLM & Generative AI Applications, RAG & Vector Search, Agentic AI, Prompt Engineering, MLOps/LLMOps, Cloud AI Platforms.
Location: Bangalore/Kolkata/Hyderabad
Role Summary
We are seeking an experienced AI Engineer – Assistant Manager to design, build, and deploy production-grade AI/ML applications, with a focus on LLM-based and agentic systems. This is a hands-on engineering role — the ideal candidate is deeply technical (writing code, building pipelines, shipping models) while beginning to guide junior engineers through influence and review (not formal management), and owning delivery of AI initiatives.
Experience
6–8 years of experience in software/ML engineering, including 2+ years building and deploying LLM-based or generative AI applications in production
Hands-on experience with LLM APIs, RAG architectures, and vector databases
Experience integrating AI/ML workflows with cloud platforms (AWS/Azure/GCP)
Proven ability to work independently and drive technical decisions with minimal oversight
Key Responsibilities
AI/ML Engineering
Design, build, and deploy machine learning and generative AI applications, including LLM-powered features (chatbots, copilots, RAG systems, agents).
Build and maintain data pipelines for model training, fine-tuning, and evaluation.
Develop and optimize RAG systems, including embedding strategies, vector database design (e.g., Pinecone, Weaviate, FAISS, pgvector), and chunking/retrieval tuning.
Integrate LLM APIs (Anthropic, OpenAI, Azure OpenAI, open-source models via Hugging Face) into production applications.
Performance, Cost & Security
Define standards for model/application performance optimization, latency, and cost efficiency at scale.
Own cost optimization recommendations and monitor API/compute usage across AI workloads.
Set data security standards for AI systems — access controls, data handling, and encryption — and monitor guardrails for reliability and safety.
Agentic AI & Applied AI
Design and build AI agents and multi-step agentic workflows using frameworks such as LangChain, LlamaIndex, LangGraph, or CrewAI.
Apply prompt engineering, fine-tuning (e.g., LoRA/QLoRA), and evaluation frameworks to improve model output quality, safety, and reliability.
Implement guardrails, evaluation harnesses, and monitoring for AI safety, hallucination detection, and output quality.
Build MLOps/LLMOps pipelines for model deployment, monitoring, versioning, and rollback (e.g., MLflow, SageMaker, Vertex AI).
Technical Leadership & Delivery
Act as a go-to technical resource on AI/LLM design decisions within the workstream.
Provide technical guidance and code/design review to junior and mid-level engineers.
Own project scoping, technical planning, and delivery of AI initiatives within a workstream.
Partner with product, data engineering, and business stakeholders to translate requirements into scalable technical solutions.
Produce technical documentation, evaluation reports, and knowledge-transfer materials to ensure continuity of AI systems.
Stakeholder Management
Collaborate with business and technology stakeholders to define AI roadmaps and use cases.
Conduct demos, training sessions, and enablement workshops on AI/LLM capabilities.
Mentor junior engineers and support proposal/pre-sales activities where applicable.
Manage project deliverables, risks, and client/stakeholder communications.
Preferred Certifications
Microsoft Certified: Azure AI Engineer Associate (AI-102)
AWS Certified Machine Learning – Specialty
Google Professional Machine Learning Engineer
Education
Bachelor's Degree in Computer Science, Machine Learning, Data Science, Information Technology, or related discipline.

Work arrangement
Yes