Associate ML Infrastructure Engineer

• Core Development & Architecture: Analyze, design, develop, test, and implement complex applications. Build enterprise-level applications and custom integrations. Design, code, debug, and document software solutions. Evaluate system interdependencies and impacts of changes.
• AI & Advanced Systems: Develop and deploy agentic AI systems. Build frameworks for generative AI use cases. Research, test, and optimize AI models and agent frameworks. Monitor and improve AI behavior in production environments.
• Technical Leadership (20%): Lead integration of applications across business systems. Design scalable structures and solutions for enterprise software.
• Consulting & Collaboration (15%): Act as internal consultant and mentor. Work with stakeholders to define business and technical requirements. Ensure alignment with IT strategy and architecture standards.
• Strategic Design & Innovation (15%): Recommend long-term IT and architecture improvements. Evaluate build vs. buy decisions. Contribute to data and component architecture design.
• System Development & Problem Solving (15%): Solve complex business and technical problems. Develop new approaches, techniques, and data sources.
• Standards & Lifecycle Management (15%): Define development standards and best practices. Participate in full SDLC (design deployment support). Ensure timely and budget-conscious delivery.
• Leadership & Mentorship (15%): Guide junior developers and analysts. Lead or coordinate complex projects. Resolve cross-team technical issues.
• Testing & Documentation (5%): Perform testing, debugging, and validation. Maintain technical documentation.

• Bachelor's degree in Computer Science, IT, or related field OR 4 years relevant experience OR Associate's degree + 2 years relevant experience.
• 8+ years in application development, systems testing, or related fields. 3 6 years in AI/ML or related domains.
• Technical Skills:

• Core Technologies: Python (advanced proficiency), JavaScript / TypeScript, API design, AI / ML & Agentic Systems, Generative AI development (end-to-end), Agentic AI concepts (reasoning, planning, tool use), Prompt engineering, RAG, and AI system design.
• Experience with AI models (e.g., OpenAI, Claude).
• Frameworks & Tools: LangChain, LangGraph, and similar frameworks. AI agent development tools and ecosystems.
• Infrastructure & DevOps: AWS (preferred) or other cloud platforms, CI/CD pipelines, Docker & Kubernetes, GitHub and modern development workflows.
• Systems Knowledge: Multi-platform environments (mainframe, midrange, PC/LAN), Software architecture and system integration, Performance monitoring and optimization.
• Nice-to-Have Skills: FastAPI or Flask, SQL and database experience, AutoGen, MCP, embeddings, knowledge stores, Reinforcement learning and planning algorithms, Multi-agent systems, Multi-cloud experience (AWS, Azure, Google Cloud Platform), Model fine-tuning and advanced prompt engineering.

• Soft Skills: Strong analytical and problem-solving abilities. Effective verbal and written communication. Ability to work under pressure in fast-paced environments. Team collaboration and leadership skills. Attention to detail. Strong interpersonal and relationship-building skills.

Work Environment: Fast-paced, project-oriented environment. Multi-platform technical ecosystem. May require occasional 24/7 responsiveness. Strong focus on innovation, collaboration, and customer needs.

Day-to-Day Activities: Hands-on Python development for AI systems. Designing and implementing AI architectures. Integrating AI agents with backend services. Deploying solutions via CI/CD pipelines. Researching and testing new AI models/frameworks. Collaborating with data scientists and stakeholders. Monitoring and improving production AI systems.

Team & Culture: Innovative, collaborative, and fast-paced. Focused on building next-generation enterprise AI solutions. Emphasis on creativity, continuous learning, and impact. Opportunity to shape long-term AI strategy.

Requires strong hands-on Python skills and a deep understanding of end-to-end RAG pipeline design. Must explain the full RAG implementation and Python programming proficiency.