AI Architect
- Compensation
- Not disclosed
- Location
- Pune, MH, India
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 12+ years
- Category
- Architecture
- Posted
Skills
Must have
- · 12+ 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.
Nice to have
- · Experience with hybrid/multi‑cloud deployments and avoiding provider lock‑in.
- · Familiarity with LangGraph, agentic safety patterns, and adversarial robustness testing.
- · Prior exposure to financial data or private markets / regulated data handling.
- · Experience with model explainability tools (SHAP, LIME, integrated gradients) and bias/fairness testing.
- · Familiarity with Terraform/ARM for infra as code and GitOps workflows.
Full description
Job Title: AI Architect
Location: India – Pune, Bangalore
Experience Required: 12+ years
Engagement: [Full-time]
Project Type: High-visibility R&D initiative in financial research & data intelligence
Job Summary: We are seeking an experienced, hands‑on AI Architect (12+ years) to lead technical delivery and client engagement for 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:
· 12+ years in software engineering/AI with demonstrable, hands‑on production experience.