Design and develop algorithms for generative models using deep learning techniques
Design and build LLM-powered applications for internal and/or customer-facing use cases
Develop and productionize RAG pipelines using enterprise data sources, vector databases, and retrieval systems
Build and optimize AI agents / agentic workflows for task automation, reasoning, and orchestration
Integrate model providers such as OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and open-source models where appropriate
Create robust evaluation frameworks for response quality, factuality, latency, safety, and reliability
Implement prompt engineering, structured outputs, tool calling, and model optimization strategies
Deploy scalable AI services to cloud environments using modern software engineering and MLOps practices
Build monitoring, observability, and feedback loops for model and application performance in production
Establish and maintain guardrails, responsible AI practices, and security controls for enterprise AI systems
Collaborate with product managers, designers, and business stakeholders to identify high-impact AI opportunities
Mentor other engineers and contribute to architecture, technical direction, and engineering best practices

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, Machine Learning, or a related field
5+ years of software engineering, machine/deep learning engineering, or applied AI experience
2+ years of hands-on experience building and deploying Generative AI / LLM-based systems in production
Strong programming skills in Python and experience with backend/API development
Experience with LLM application development, including prompt engineering, RAG, tool use, and structured output design
Experience in optimizing RAG pipelines using both structured and unstructured data
Experience with orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent
Experience in generative AI techniques such as GANs, and VAEs
Hands-on experience with vector databases / retrieval systems such as Pinecone, Weaviate, Chroma, FAISS, Elasticsearch, or Azure AI Search
Experience with cloud platforms such as AWS, GCP, or Azure
Experience with Docker, Kubernetes, CI/CD, and production deployment practices
Strong understanding of software architecture, scalability, reliability, and distributed systems
Experience building evaluation, testing, and monitoring for AI systems
Strong communication skills and ability to work closely with technical and non-technical stakeholder

Preferred Qualifications

Experience fine-tuning or adapting open-source LLMs
Advanced knowledge of natural language processing for text generation tasks
Experience with PyTorch, TensorFlow, JAX, or related ML frameworks
Experience with MLOps tools such as MLflow, SageMaker, Vertex AI, Azure ML, Kubeflow, or similar
Experience building multi-agent systems or advanced orchestration workflows
Experience with AI safety, guardrails, red-teaming, privacy, and governance
Familiarity with search, ranking, recommendation, conversational AI, or enterprise knowledge systems
Experience in customer-facing or enterprise SaaS products
Experience in semiconductor/manufacturing, retail and e-commerce sectors

What Success Looks Like

Deliver production-ready GenAI features that improve user experience and business outcomes
Build reliable and scalable AI systems with strong quality, latency, and cost performance
Establish best practices for evaluation, observability, and responsible AI development
Help define the company’s long-term Generative AI architecture and roadma

All your information will be kept confidential according to EEO guidelines.

*** NO C2C ***

Sr. Gen AI Engineer