Sr. Gen AI Engineer
About The Position
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
Requirements
- 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
Nice To Haves
- 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
Responsibilities
- 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
Benefits
- Medical Insurance
- Vision Insurance
- Dental Insurance
- 401(k)
- Paid Sick hours
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Mid Level
