AI Engineer - Agentic AI & LLMOps
Location: Bhubaneswar Odisha
Department: Data Science
Job Type: Full-time
Experience: 45 years preferred
About StratLytics
StratLytics is a data science AI and analytics consulting firm building production-grade analytics and AI solutions for enterprise clients across financial services fin-tech risk analytics manufacturing retail and other sectors. We combine data engineering machine learning Generative AI decision intelligence and business workflow automation to solve high-impact business problems.
Role Overview
We are hiring an AI Engineer - Agentic AI & LLMOps to support a financial-services AI initiative in North America.
This is a hands-on applied AI engineering role focused on building production-grade AI systems not generic chatbot demos. The engineer will work on backend APIs Agentic AI workflows LLM orchestration structured JSON outputs evaluation pipelines audit logging and deployment-ready AI services.
The ideal candidate should have strong Python backend engineering skills practical experience with LLM APIs and the ability to build reliable testable maintainable AI applications.
Key Responsibilities
- Build backend services using Python FastAPI and Pydantic.
- Develop Agentic AI workflows using LangGraph LangChain AutoGen or similar frameworks.
- Integrate LLM APIs such as OpenAI Azure OpenAI or AWS Bedrock.
- Design structured JSON outputs for AI-generated recommendations.
- Implement prompt versioning schema validation response validation and logging.
- Build multi-step AI workflows involving specialist agents and synthesis logic.
- Implement timeout handling retry logic fallback behavior and error management.
- Work with data engineers to consume validated and normalized business data payloads.
- Build audit logging traceability and observability into AI workflows.
- Support evaluation and regression testing of prompts agents and outputs.
- Containerize and deploy backend services using Docker and CI/CD workflows.
- Collaborate with data scientists AI scientists data engineers and business SMEs.
- Write clean modular maintainable and well-tested code.
Ideal Candidate Profile
The ideal candidate is a strong Python backend engineer who has moved into applied AI engineering. They should understand that production AI is not just prompt writing. It requires API design schema validation error handling observability testing deployment discipline and secure handling of business-critical data.
The candidate should be comfortable building AI systems where outputs must be structured traceable review-able and reliable.
Requirements
Required:
- 45 years of hands-on software engineering experience
- Strong programming skills in Python
- Hands-on experience with FastAPI or Flask
- Experience with Pydantic JSON Schema or structured data validation
- Good understanding of REST API design and backend service development
- Practical experience integrating LLM APIs (OpenAI Azure OpenAI AWS Bedrock or similar)
- Working knowledge of PostgreSQL or similar relational databases
- Experience with Docker and containerised application development
- Strong Git / GitHub workflow
- Ability to write clean modular maintainable and testable code
- Ability to work from StratLytics Bhubaneswar office 5 days a week
Preferred:
- Exposure to LangChain LangGraph AutoGen LlamaIndex or similar Agentic AI frameworks
- Experience with structured AI outputs function calling tool calling or schema-constrained responses
- Experience with logging error handling debugging and unit testing
- Understanding of LLMOps concepts (prompt versioning evaluation observability regression testing)
- Prior exposure to financial services fin-tech credit risk compliance or risk analytics
Benefits
Compensation & Growth
- Competitive compensation based on experience and capability.
- Performance-linked variable pay and annual review.
- Clear growth path into senior AI engineering AI architecture and applied AI leadership roles.
Health & Wellbeing
- Health insurance for you and your dependents.
- Provident Fund and Gratuity in line with Indian statutory requirements.
Learning & Tools
- Learning & development budget for books courses and certifications.
- Modern engineering stack: FastAPI Pydantic LangGraph LangChain OpenAI / Azure OpenAI / AWS Bedrock Docker PostgreSQL CI/CD.
- Production-grade hardware (high-spec developer laptop).
The Work Itself
- High-impact role on AI initiative for a north american customer.
- Hands-on production Agentic AI and LLMOps not POCs.
- Real enterprise AI systems: APIs structured outputs evaluation audit logging deployment.
- Direct collaboration with senior data scientists AI scientists data engineers and business stakeholders.
- Contribute to StratLytics growing AI and analytics practice from our Bhubaneswar office.
Required Skills:
45 years of hands-on software engineering experience preferably in Python backend development and applied AI systems. Strong programming skills in Python. Hands-on experience with FastAPI or Flask. Experience with Pydantic JSON Schema or structured data validation. Good understanding of REST API design and backend service development. Practical experience integrating LLM APIs such as OpenAI Azure OpenAI AWS Bedrock or similar. Exposure to LangChain LangGraph AutoGen LlamaIndex or similar Agentic AI frameworks. Experience with structured AI outputs function calling tool calling or schema-constrained responses. Working knowledge of PostgreSQL or similar relational databases. Experience with Docker and containerized application development. Strong Git / GitHub workflow. Ability to write clean modular maintainable and testable code. Experience with logging error handling debugging and unit testing. Understanding of LLMOps concepts such as prompt versioning evaluation observability and regression testing. Ability to work from Bhubaneswar in an on-site or hybrid model. Prior exposure to financial services fin-tech credit risk compliance or risk analytics is preferred but not mandatory.
Required Education:
/ B.E. / / MCA / . in Computer Science Data Science Artificial Intelligence Machine Learning Information Technology or a related hands-on project experience may compensate for formal specialization.
AI Engineer - Agentic AI & LLMOpsLocation: Bhubaneswar OdishaDepartment: Data ScienceJob Type: Full-timeExperience: 45 years preferredAbout StratLyticsStratLytics is a data science AI and analytics consulting firm building production-grade analytics and AI solutions for enterprise clients across fin...
AI Engineer - Agentic AI & LLMOps
Location: Bhubaneswar Odisha
Department: Data Science
Job Type: Full-time
Experience: 45 years preferred
About StratLytics
StratLytics is a data science AI and analytics consulting firm building production-grade analytics and AI solutions for enterprise clients across financial services fin-tech risk analytics manufacturing retail and other sectors. We combine data engineering machine learning Generative AI decision intelligence and business workflow automation to solve high-impact business problems.
Role Overview
We are hiring an AI Engineer - Agentic AI & LLMOps to support a financial-services AI initiative in North America.
This is a hands-on applied AI engineering role focused on building production-grade AI systems not generic chatbot demos. The engineer will work on backend APIs Agentic AI workflows LLM orchestration structured JSON outputs evaluation pipelines audit logging and deployment-ready AI services.
The ideal candidate should have strong Python backend engineering skills practical experience with LLM APIs and the ability to build reliable testable maintainable AI applications.
Key Responsibilities
- Build backend services using Python FastAPI and Pydantic.
- Develop Agentic AI workflows using LangGraph LangChain AutoGen or similar frameworks.
- Integrate LLM APIs such as OpenAI Azure OpenAI or AWS Bedrock.
- Design structured JSON outputs for AI-generated recommendations.
- Implement prompt versioning schema validation response validation and logging.
- Build multi-step AI workflows involving specialist agents and synthesis logic.
- Implement timeout handling retry logic fallback behavior and error management.
- Work with data engineers to consume validated and normalized business data payloads.
- Build audit logging traceability and observability into AI workflows.
- Support evaluation and regression testing of prompts agents and outputs.
- Containerize and deploy backend services using Docker and CI/CD workflows.
- Collaborate with data scientists AI scientists data engineers and business SMEs.
- Write clean modular maintainable and well-tested code.
Ideal Candidate Profile
The ideal candidate is a strong Python backend engineer who has moved into applied AI engineering. They should understand that production AI is not just prompt writing. It requires API design schema validation error handling observability testing deployment discipline and secure handling of business-critical data.
The candidate should be comfortable building AI systems where outputs must be structured traceable review-able and reliable.
Requirements
Required:
- 45 years of hands-on software engineering experience
- Strong programming skills in Python
- Hands-on experience with FastAPI or Flask
- Experience with Pydantic JSON Schema or structured data validation
- Good understanding of REST API design and backend service development
- Practical experience integrating LLM APIs (OpenAI Azure OpenAI AWS Bedrock or similar)
- Working knowledge of PostgreSQL or similar relational databases
- Experience with Docker and containerised application development
- Strong Git / GitHub workflow
- Ability to write clean modular maintainable and testable code
- Ability to work from StratLytics Bhubaneswar office 5 days a week
Preferred:
- Exposure to LangChain LangGraph AutoGen LlamaIndex or similar Agentic AI frameworks
- Experience with structured AI outputs function calling tool calling or schema-constrained responses
- Experience with logging error handling debugging and unit testing
- Understanding of LLMOps concepts (prompt versioning evaluation observability regression testing)
- Prior exposure to financial services fin-tech credit risk compliance or risk analytics
Benefits
Compensation & Growth
- Competitive compensation based on experience and capability.
- Performance-linked variable pay and annual review.
- Clear growth path into senior AI engineering AI architecture and applied AI leadership roles.
Health & Wellbeing
- Health insurance for you and your dependents.
- Provident Fund and Gratuity in line with Indian statutory requirements.
Learning & Tools
- Learning & development budget for books courses and certifications.
- Modern engineering stack: FastAPI Pydantic LangGraph LangChain OpenAI / Azure OpenAI / AWS Bedrock Docker PostgreSQL CI/CD.
- Production-grade hardware (high-spec developer laptop).
The Work Itself
- High-impact role on AI initiative for a north american customer.
- Hands-on production Agentic AI and LLMOps not POCs.
- Real enterprise AI systems: APIs structured outputs evaluation audit logging deployment.
- Direct collaboration with senior data scientists AI scientists data engineers and business stakeholders.
- Contribute to StratLytics growing AI and analytics practice from our Bhubaneswar office.
Required Skills:
45 years of hands-on software engineering experience preferably in Python backend development and applied AI systems. Strong programming skills in Python. Hands-on experience with FastAPI or Flask. Experience with Pydantic JSON Schema or structured data validation. Good understanding of REST API design and backend service development. Practical experience integrating LLM APIs such as OpenAI Azure OpenAI AWS Bedrock or similar. Exposure to LangChain LangGraph AutoGen LlamaIndex or similar Agentic AI frameworks. Experience with structured AI outputs function calling tool calling or schema-constrained responses. Working knowledge of PostgreSQL or similar relational databases. Experience with Docker and containerized application development. Strong Git / GitHub workflow. Ability to write clean modular maintainable and testable code. Experience with logging error handling debugging and unit testing. Understanding of LLMOps concepts such as prompt versioning evaluation observability and regression testing. Ability to work from Bhubaneswar in an on-site or hybrid model. Prior exposure to financial services fin-tech credit risk compliance or risk analytics is preferred but not mandatory.
Required Education:
/ B.E. / / MCA / . in Computer Science Data Science Artificial Intelligence Machine Learning Information Technology or a related hands-on project experience may compensate for formal specialization.
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