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Job Description of : Forward deployed AI Engineer

Job Title : Manager/ Sr Manager

Department : Information Technology

Reports To : AVP

Experience : 8 to 12 years

Preferred Qualification : BE/ B Tech/ MCA

Required Qualification : BE/ B Tech/ MCA

1. Area of operations:  "AI and software engineering"
2. Skills, Knowledge and Training's: 
- Proven experience on AI and software engineering with deep fluency in Python, modern AI frameworks, vector and graph databases.
- Deep expertise in building and deploying production-grade AI systems, workflows and models 
- Strong data engineering and analytics foundations

Core Competencies :

1.	Data Engineering & Pipelines: Expertise in building complex data pipelines of structured and unstructured data that can be consumed by AI applications. This includes textual, image data preprocessing capabilities.
2.	Image and Document handling: Ability and Expertise in extracting, processing and parsing complex documents, images that can be further consumed by AI agents and AI applications 
3.	Advanced RAG Architectures: Expertise in building and optimizing Retrieval-Augmented Generation systems. This includes advanced chunking strategies, multi-stage retrieval, re-ranking, and hybrid search.
4.	Agentic Workflows & Tool Use: Experience designing multi-agent systems, function calling, stateful orchestration frameworks, and error-handling loops.
5.	Evaluation & Guardrails: Proficiency in implementing systematic LLM evaluation frameworks (e.g., Ragas, TruLens) and safety/compliance guardrails (e.g., NeMo Guardrails, Llama Guard) to control hallucination and data leakage.
6.	Vector & Graph Databases: Knowledge of graph and vector retrieval and indexing. Prior production experience in using Vector and Graph databases for faster and accurate data retrieval.  
7.	Enterprise Integration & Security: Ability to securely hook AI applications into legacy enterprise architectures, including REST/gRPC APIs, IAM/OAuth systems, and VPC networks.
8.	Cloud & Containerization: Fluency with containerized infrastructure (Docker, Kubernetes) and enterprise cloud platforms (AWS, GCP, Azure) to deploy scalable, highly available workloads.
9.	Production-Grade Software Engineering: Writing clean, modular, and asynchronous Python code adhering to strict CI/CD pipelines, automated testing, and logging/observability standards (e.g., OpenTelemetry, Arize, LangSmith).
10.	Production Data and Model Drift Monitoring and Management: Past proven expertise in detecting shifts in user behaviour, intent, or input format. Monitoring the underlying data source, the data distribution drifts. Evaluating changes in model behaviour over time, especially when using third-party APIs.
11.	Explainability & Lineage: Past experience in showcasing deep transparency in AI decisions making process and logging clear logical reasoning chain. 
12.	Project Management: Take end to end responsibility of the requirements design, build, development, deployment, validation, monitoring and delivery the AI use cases.

Functional Competencies :

Financial Market

Job Purpose :

The Forward deployed (AI) Engineer would bridge the gap between CCIL’s AI platform capabilities and CCIL’s business understanding and execution of Enterprise AI use cases. The candidates shall be hands-on full stack AI engineers who can work closely with CCIL core business departments, right from initial discovery and building production scale AI systems to scaling AI use cases on CCIL’s AI platform. The candidates shall take complete ownership of development, deployment and delivery of the AI use cases and also ensure that efficient business data pipelines are built to compliment the AI platform

Area of Operations :

AI and software engineering

Key Responsibility :

1.	Development: Complete ownership of building end-to-end AI applications across the stack, including responsive user interfaces, robust backend services, and scalable database schemas
2.	End-to-End Deployment: Design, develop, test, deploy and maintain AI Applications, AI Agents and GenAI applications using LLMs, RAG, tool / API Integration and agentic workflows, integrated through CCIL’s enterprise AI Gateway and orchestration platform.
3.	Build and maintain efficient data pipelines: Build end to end production scale efficient Data Pipelines to meet data requirements for the AI use cases in production
4.	Feedback Loop & Core Product Evolution: Identify recurring pain points and operational bottlenecks, feeding insights back to our core product and engineering teams.
5.	Codify Best Practices: Build and publish reusable deployment patterns, evaluation suites, guardrails, and architectural blueprints that scale across future engagements. 

Any Other Requirement :

1.	Should be a good team player.
2.	Should be excellent in communication and data analytics.
3.	Would be required to work with multiple projects / teams concurrently