Job Description Summary

Job Description

Roles and Responsibilities

ML/DL/AI Solution Development

• Design develop and implement AI-based solutions such as RAG pipeline document query engines semantic analysers.
• Build prompt orchestration retrieval augmentation (RAG) solutions and context-management pipelines for industrial use cases.
• Develop scalable and secure solutions which utilize an GenAI based solution underneath to improve internal software systems.
• Optimize LLM inference performance token usage latency and model selection based on functional and cost constraints.
• Implement validation frameworks for LLM outputs including rule-based statistical or semantic validation alignment checks and performance monitoring.

• Support or lead development of ML models (classification regression anomaly detection time-series insights) for reliability operations and maintenance use cases.
• Collaborate with data scientists to convert analytical logic into stable production code and cloud-ready services.
• Develop pipelines integrating historian/SCADA/CMMS/APM datasets into AI workflows.

Software Engineering & Integration

• Engineer robust backend services in Python with clean architecture modular design and high-quality code practices.
• Build and maintain REST APIs microservices and integration points with industrial systems and enterprise platforms.
• Work with product and domain teams to embed AI/LLM features directly into industrial software applications.
• Contribute to UI/UX workflows (optional) for AI-driven features such as chatbot interfaces AI copilots or operator assistance tools.

Quality Validation & Governance

• Design automated evaluation frameworks for LLM results: hallucination checks accuracy scoring domain constraint validation and response explainability.
• Maintain experiment tracking version control and model documentation to ensure reproducibility and governance.
• Support secure handling of operational and proprietary data with compliance to organizational and industry standards.
• Conduct performance testing error monitoring and continuous improvement of deployed AI services.

Collaboration & Innovation

• Partner closely with domain SMEs (maintenance reliability operations) to translate use cases into AI-driven workflows.
• Collaborate with platform/cloud engineering teams to deploy LLM services at scale (containers serverless GPU-enabled workloads).
• Actively explore new LLM capabilities vector databases fine-tuning methods and industrial AI patterns driving innovation in the team.
• Mentor junior developers and support internal AI capability-building initiatives.

Required Skills & Experience

• 58 years of professional software engineering experience including 23 years building AI/ML or LLM-driven applications in production environments.
• Strong expertise in Python with deep experience in backend development RESTful API design and microservices architecture.
• 3 years of hands-on experience with FastAPI including strong knowledge of its architecture performance optimization dependency injection and asynchronous capabilities.
• Demonstrated experience developing and deploying LLM-powered applications using frameworks such as OpenAI Hugging Face LangChain LlamaIndex or similar ecosystems.
• Proven ability to design and implement LLM validation frameworks evaluation methodologies guardrails and prompt governance pipelines to ensure reliability accuracy and compliance.
• Solid understanding of LLM fundamentals including tokenization transformer architecture attention mechanisms embeddings fine-tuning approaches model constraints and context window management.
• Experience managing the end-to-end ML lifecycle including data preparation model training packaging deployment versioning monitoring and performance optimization.
• Familiarity with industrial or operational data systems (e.g. APM historian systems SCADA CMMS/EAM) is highly desirable.
• Strong working knowledge of CI/CD practices and DevOps tooling including Jenkins Docker Kubernetes and Helm.
• Experience deploying and scaling applications on AWS including infrastructure design and cloud-native architecture.
• Excellent analytical problem-solving and communication skills with the ability to collaborate effectively across engineering product and business teams.
• Exposure to frontend technologies such as Angular is a plus.

Education Qualification

• Bachelors Degree in Computer Science or STEM Majors (Science Technology Engineering and Math) with advanced experience.

Additional Information