Soni's client is seeking an Artificial Intelligence / Machine Learning Engineer to design, build, deploy, and support enterprise-scale AI and ML solutions across cloud and hybrid environments. This is a highly hands-on role focused on delivering scalable, secure, and high-performing AI solutions aligned with business and technology objectives.
The ideal candidate will have experience developing production-grade AI applications, LLM-powered solutions, and agentic workflows while collaborating with cross-functional technical and business teams.
Key Responsibilities
- Design, develop, and maintain production-grade AI and ML solutions across cloud and hybrid platforms
- Build end-to-end AI application pipelines including data ingestion, prompt engineering, model evaluation, deployment, and monitoring
- Develop and support LLM-powered applications and agentic workflows
- Collaborate with engineering, data, and business stakeholders to translate requirements into AI and ML solutions
- Write scalable, maintainable, and high-quality Python code for AI and ML applications
- Optimize performance, scalability, reliability, and cost efficiency of AI and ML services
- Integrate AI and ML applications with enterprise APIs, data lakes, data warehouses, retrieval systems (RAG), and streaming platforms
- Support AI solution lifecycle management including versioning, prompt iteration, evaluation, and production support
- Apply security, governance, compliance, and data privacy best practices across AI and ML systems
- Participate in technical architecture and solution design discussions
- Stay current on advancements in AI, ML, LLMs, cloud engineering, and orchestration technologies
Required Qualifications
- 5+ years of experience building and deploying AI and ML solutions
- Strong hands-on experience developing AI and ML applications within cloud environments
- Advanced Python programming skills focused on AI/ML engineering and LLM application development
- Experience building and supporting LLM-powered applications, agentic workflows, and AI orchestration pipelines
- Experience integrating LLM applications with enterprise data sources and retrieval systems (RAG)
- Hands-on experience with AI orchestration and observability frameworks such as LangChain, LangSmith, Pydantic AI, or similar technologies
- Strong understanding of cloud security, AI governance, data privacy, and enterprise AI compliance practices
- Experience developing scalable, production-grade cloud-native applications
Preferred Qualifications
- Experience supporting enterprise AI initiatives within professional services or regulated environments
- Familiarity with vector databases, embedding models, semantic search, and AI evaluation frameworks
- Experience with containerization and deployment technologies such as Docker and Kubernetes
- Exposure to MLOps and AI observability best practices
- Experience integrating AI systems into enterprise business workflows and knowledge management platforms