Senior Artificial Intelligence Engineer

Aptask; Pune District, Bengaluru; On-site; INR 4,500,000 - 6,500,000; Full time

Job summary

Aptask in India seeks a Sr. AI Engineer with 9+ years of hands-on experience to design and productionize LLM-based solutions (RAG, fine-tuning) on Azure.

You will lead a cross-functional delivery team and act as the technical face to clients, delivering end-to-end AI platforms. You will architect Agentic AI systems, implement MLOps pipelines, manage data retrieval layers, ensure governance and security, and mentor engineers while collaborating with product, security, and DevOps to ensure

Qualifications

• 9+ years in software engineering/AI with production experience.
• Deep experience with LLMs and RAG architectures.
• Proven track record in fine tuning LLMs (LoRA/FT/IF) and prompt engineering.
• Strong Azure AI stack experience: Azure OpenAI, Azure ML, AKS, Functions, KeyVault.

Responsibilities

• Architect and build Agentic AI systems with agents, action executors, and retrieval layers.
• Design and implement production LLM solutions (RAG, retrieval chains, prompt engineering).
• Own model deployment, serving and scaling on Azure and hybrid setups.
• Build MLOps pipelines: CI/CD for models, testing, monitoring, drift detection, rollbacks.
• Lead data pipelines for retrieval: vector stores, embeddings, privacy controls.
• Define and enforce model governance: versioning, lineage, audit trails.
• Collaborate with product, data engineering, DevOps and security for integrated delivery.
• Mentor engineers, conduct reviews and participate in client meetings.

Skills

LLM expertise

RAG architectures

Prompt engineering

Azure AI stack

Agentic frameworks

Python

FastAPI

Kubernetes

Tools

Azure OpenAI/GPT

Azure ML

AKS

LangChain

LangGraph

CI/CD for ML

Prometheus/Grafana/ELK

Job description

Job Title :- Sr. AI Engineer

Work Location:- Pune/Bengaluru

Job Type:- FTE

Job Summary:

We are seeking an experienced, hands on AI (4+ years) to Agentic AI platforms. The role owns end to end design, productionisation and operationalisation of LLM based solutions (RAG, fine tuning, model serving) on Azure, and will act as the technical face to the client while leading a cross functional delivery team.

What Success Looks like:

• Deliver production Agentic AI features that meet SLA targets for latency, throughput and reliability.
• Reduce time to value for LLM integrations via repeatable patterns, MLOps pipelines and reusable components.
• Maintain model governance, explainability and security posture appropriate for sensitive data domains.

Key Responsibilities:

• Architect and build Agentic AI systems: orchestrate agents, action executors, retrieval layers, and feedback loops.
• Design and implement LLM solutions (RAG, retrieval chains, prompt engineering, fine tuning/LoRA/P-tuning) for production use.
• Own model deployment, serving and scaling on Azure (Azure AI, Azure ML, AKS, container registries) and hybrid setups.
• Build MLOps & ModelOps pipelines: CI/CD for models and services, automated testing, monitoring, drift detection and rollbacks.
• Lead data pipelines for retrieval: vector stores, semantic search, indexing, embeddings, data privacy & access controls.
• Implement model explainability, confidence scoring, adversarial protections and prompt security (prompt injection mitigation).
• Define and enforce model governance: versioning, reproducibility, lineage, audit trails and compliance.
• Collaborate with product, data engineering, security, DevOps and UX to ensure integrated delivery and acceptance.
• Mentor and upskill engineers; conduct technical reviews and pair programming; recruit when required.
• Act as primary technical contact for clients: present designs, lead architecture reviews, and support RFP/interview processes.

Required Skills & Experience:

• 9 + years in software engineering/AI with demonstrable, hands on production experience.
• Deep experience with LLMs and RAG architectures: retrieval design, vector DBs (Pinecone/Weaviate/Milvus), embeddings.
• Proven track record in fine tuning/customising LLMs (LoRA, full fine tune, instruction tuning) and prompt engineering.
• Strong Azure AI stack experience: Azure OpenAI/GPT, Azure ML, AKS, Azure Functions, KeyVault, Data Factory/Synapse.
• Expertise in Agentic frameworks and orchestration (LangChain, LangGraph, custom agent frameworks).
• Production MLOps/ModelOps: CI/CD for models, model registry, automated testing, monitoring (Prometheus/Grafana/ELK), drift detection.
• Backend engineering: Python, FastAPI, microservices, Docker, Kubernetes, gRPC/REST, event-driven architectures.
• Data engineering basics: SQL/NoSQL, ETL, schema design, data lineage and data privacy controls.
• Security & compliance: secrets management, access controls, vulnerability remediation, data encryption in transit & at rest.