Duties
We are seeking a highly skilled Senior GenAI Engineer to design, build, and scale production-grade Generative AI systems and intelligent applications for enterprise and client-facing use cases.
This role is ideal for an AI-native software engineer who combines strong backend engineering fundamentals with deep practical experience in LLMs, AI agents, Retrieval-Augmented Generation (RAG), and modern AI infrastructure.
You will work across the full AI application lifecycle — from rapid prototyping and experimentation to deployment, observability, evaluation, optimization, and production scaling. The ideal candidate thrives in fast-paced environments, can navigate ambiguity, and is passionate about building reliable, high-impact AI systems.
Key Responsibilities
- Design, develop, and deploy scalable GenAI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and workflow orchestration frameworks.
- Build production-grade AI systems integrating structured and unstructured enterprise data sources.
- Architect and optimize end-to-end AI pipelines including retrieval, embeddings, vector search, prompt orchestration, evaluation, observability, and monitoring.
- Develop AI-powered copilots, assistants, automation workflows, and autonomous agent systems for business-critical use cases.
- Design hybrid AI systems combining deterministic workflows with autonomous agent behaviors.
- Build multi-agent orchestration workflows with tool calling, memory management, and task planning capabilities.
- Implement tracing, telemetry, observability, and monitoring for AI workflows and agent systems.
- Build automated evaluation pipelines, benchmark suites, regression testing frameworks, and synthetic test datasets for GenAI applications.
- Improve system reliability by reducing hallucinations, optimizing retrieval quality, and implementing AI safety and guardrail mechanisms.
- Optimize inference cost, latency, throughput, and scalability of production AI systems.
- Rapidly prototype and iterate on AI workflows based on user feedback, experimentation, and production telemetry.
- Own AI features and systems end-to-end from prototype through production adoption and operational excellence.
- Collaborate closely with business stakeholders, product managers, platform teams, and data engineers to translate ambiguous business problems into scalable AI solutions.
- Mentor junior engineers and contribute to AI engineering best practices, reusable frameworks, and platform standards.
- Stay current with emerging advancements in LLMs, agentic AI, multimodal systems, open-source models, and AI infrastructure ecosystems
Work Environment
- Fast-paced, AI-first engineering environment
- Opportunity to build cutting-edge GenAI platforms and intelligent systems at enterprise scale
- High ownership, rapid iteration, and strong engineering culture
Skills
Required Skills & Experience
- 6–9+ years of strong software engineering experience, including backend systems, APIs, distributed systems, and production platform development.
- 3+ years of hands-on experience building and deploying production-grade GenAI or LLM-powered applications.
- Strong expertise in Python and modern AI application frameworks.
- Experience building scalable APIs, microservices, and cloud-native applications.
- Strong understanding of production system design, scalability, resiliency, and observability principles.
Hands-on Experience With
- LLM APIs and open-source models
- Retrieval-Augmented Generation (RAG)
- AI agents and tool-calling architectures
- Multi-agent orchestration systems
- Prompt engineering and prompt optimization
- Embedding models and vector databases
- AI evaluation and observability frameworks
Workflow orchestration and automation systems
- Experience working with multiple foundation model providers and open-source LLM ecosystems.
- Experience with large-scale datasets, relational databases, and advanced SQL.
- Strong understanding of vector search, semantic retrieval, ranking, chunking strategies, and context optimization.
- Experience integrating GenAI systems with enterprise platforms, APIs, and data ecosystems.
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
- Strong debugging, optimization, and production troubleshooting capabilities.
- Excellent communication skills with the ability to explain complex AI concepts to both technical and non-technical stakeholders.
- Strong problem-solving mindset with the ability to operate effectively in ambiguous and fast-moving environments.
- Proven ability to lead technical initiatives, mentor teams, and drive execution across cross-functional teams.
Preferred Qualifications
- Experience with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel, AutoGen, or similar.
- Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or pgvector.
- Experience with AI observability and evaluation platforms such as LangSmith, Weights & Biases, Arize, Helicone, Phoenix, or similar.
- Experience with orchestration and deployment tools such as Docker, Kubernetes, Ray, MLflow, Airflow, or Temporal.
- Experience with model fine-tuning, PEFT, LoRA, and open-source LLM deployment.
- Familiarity with inference optimization techniques including caching, routing, batching, quantization, and model serving optimization.
- Experience building agentic workflows with memory, planning, reflection, and tool execution patterns.
- Experience with Streamlit, Gradio, React, or modern AI application frontends.
- Knowledge of AI security, prompt injection mitigation, guardrails, and responsible AI practices.
- Exposure to multimodal AI systems (text, image, audio, video) is a plus.
- Prior consulting or client-facing delivery experience is highly desirable.
Technology Stack
- Python
- FastAPI
- SQL
- Snowflake
- Streamlit / Gradio / React
- LangChain / LangGraph / LlamaIndex
- OpenAI / Anthropic / Gemini APIs
- Vector Databases (Pinecone, Weaviate, pgvector, FAISS)
- Docker / Kubernetes
- MLflow / LangSmith / W&B
- AWS / Azure / GCP
Education
Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or related quantitative field required.
Master's degree or specialization in AI/ML/Data Science is a strong plus.
