Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), Automation, Bridge Building, Business Case, Cloud Computing, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Data Science, Docker, Enterprise Applications, GCP (Good Clinical Practices), Java, Machine Learning, Microservices, Microsoft Windows Azure, NoSQL, Offshoring, Performance Tuning/Optimization, Predictive Modeling, Problem Solving Skills, Production Support, SQL (Structured Query Language), Scalable System Development, Software Engineering, Team Player, Use Cases
Job Description
AI Engineer
Must Have Technical/Functional Skills
- 4 8+ years of experience in software engineering, ML engineering, or AI solution development
- Strong proficiency in Java and experience
- Hands on experience with Generative AI / LLMs, including RAG, embeddings, prompt engineering, and agents
- Solid understanding of data engineering concepts, SQL/NoSQL, and feature pipelines
- Experience deploying AI solutions on cloud platforms (GCP preferred; AWS/ Azure acceptable)
- Familiarity with Docker, Kubernetes, and CI/CD pipelines
- Strong problem solving, communication, and stakeholder collaboration skills
Roles & Responsibilities
We are seeking a highly skilled Java + AI Engineer to design, build, and deploy scalable AI and Generative AI solutions that solve real business problems. The role bridges data science, software engineering, and cloud platforms to operationalize machine learning, LLM based applications, and intelligent automation across the enterprise.
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Key Responsibilities
- Design, develop, and deploy AI/ML and Generative AI solutions including LLM based applications, RAG pipelines, agents, and predictive models
- Translate business use cases into production ready AI solutions with measurable outcomes
- Deep knowledge in Java streams technology
- Implement LLM orchestration, prompt engineering, vector search, embeddings, and model fine tuning
- Develop scalable APIs and microservices to integrate AI capabilities into enterprise applications
- Collaborate with Data Engineers, Data Scientists, Product Owners, and Cloud teams across onshore offshore models
- Implement MLOps / LLMOps practices including CI/CD, monitoring, versioning, model governance, and observability
- Ensure Responsible AI, security, compliance, and data privacy by design
- Support production deployments, performance tuning, and continuous improvement of AI systems