Lead design, development, and production deployment of LLM-powered applications, RAG pipelines, agentic workflows, and evaluation/monitoring systems. Collaborate cross-functionally to translate business needs into scalable, secure AI services and mentor engineering teams while enforcing responsible AI practices and production-grade MLOps.
Company Description
On-Site
1yr contract
$10950/month
About the Role
We are seeking a Senior Generative AI Engineer to design, build, and deploy production-grade AI applications powered by large language models (LLMs). In this role, you will lead the end-to-end development of Generative AI solutions, including LLM-powered applications, retrieval-augmented generation (RAG) systems, agentic workflows, model evaluation pipelines, and production infrastructure.
You will work cross-functionally with product, finance, data, and business stakeholders to translate real-world business problems into scalable AI systems that deliver measurable value.
Job Description
- 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
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
All your information will be kept confidential according to EEO guidelines.
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