Senior Artificial Intelligence Engineer

Targeticon Digital Services; Mumbai City; On-site; INR 3,500,000 - 5,200,000; Full time

Job summary

Targeticon Digital Services seeks a Senior AI Architect to lead end-to-end AI architecture for BFSI products, spanning data pipelines, GPU utilization, and deployment on AWS cloud and on‑prem environments.

The role demands 15+ years of hands-on AI experience and strong leadership to guide tech roadmaps, model selection (RAG vs fine‑tuning), and secure integration with existing IT systems.

Qualifications

• Minimum 15 years hands-on AI experience.
• Seasoned architect with end-to-end design and deployment capabilities.
• Experience guiding AI initiatives in BFSI environments.

Responsibilities

• Architect scalable AI systems spanning data pipelines, GPUs, and deployment across cloud/on‑prem environments.
• Choose between RAG or fine-tuning and select tools for specific use cases.
• Ensure integration with existing IT infrastructure and uphold security standards.
• Implement MLOps, monitor latency and hallucinations, and establish automated guardrails.
• Collaborate with executives, data scientists, and engineers to align tech Roadmaps with ROI.

Skills

Data Management

Cloud & Infrastructure

Leadership & Strategy

Tools

LangGraph

TensorFlow

PyTorch

Docker

Kubernetes

Job description

Position: Senior AI Architect

Location: Colaba, Mumbai

No. of positions: 2

Essential Experience: Minimum of 15 years of hands‑on experience with AI

Key Responsibilities / Accountabilities:

Summary: We are looking for a seasoned Sr. AI Architect to join our dynamic AI team working on advanced products for the Banking, Financial Services, and Insurance (BFSI) sector. This role demands a strategic thinker with strong technical depth who can lead end-to-end architecture design, deployment planning, and scaling of AI-driven systems, both on AWS cloud and on-premise environments.

Core Responsibilities

• Architectural Design: Build scalable, cost‑aware systems handling data pipelines, GPU utilization, and model deployment across hybrid or cloud environments.
• Technology & Model Selection: Choose between RAG (Retrieval‑Augmented Generation) or fine‑tuning, and select the best tools (e.g., LangGraph, TensorFlow, PyTorch) for specific use cases.
• System Integration: Guarantee AI solutions integrate seamlessly with existing IT infrastructure and maintain security standards.
• Governance & Lifecycle Management: Implement MLOps (ModelOps), monitor for latency and hallucinations, and establish automated guardrails.
• Stakeholder Management: Collaborate with executives, data scientists, and engineers to align technology roadmaps with business ROI.

Key Skills & Requirements

• Data Management: Expertise in vector databases, data readiness, processing large networks, and orchestration frameworks.
• Data Management: Expertise in vector databases, data readiness, and processing largedatasets.
• Cloud & Infrastructure: Familiarity with major cloud providers (AWS, Azure, GCP) and containerization/orchestration tools (Docker, Kubernetes).
• Leadership & Strategy: Exceptional communication and analytical skills to bridge the gap between technical teams and business stakeholders.

Excellent verbal communication skills

Good problem-solving skill