Job Description
Job Title: Lead GenAI Engineer - Banking Client Exp required
Location: Manhattan, NY (3 days a week onsite)
Duration: 6 months rolling contract
£70/Hr on C2C
GC and USC Candidates only
Interview Process:- 2 Rounds- Interview will be in person in NY office
Description
We are looking for a highly skilled and hands-on GenAI Engineer to drive the design, development, and deployment of enterprise-scale Generative AI solutions. The ideal candidate will have strong expertise in Agentic AI workflows, RAG architectures, LLM orchestration, workflow automation, and scalable AI platform engineering. This role requires both technical depth and the ability to translate complex business problems into practical AI-driven solutions.
Key Responsibilities
- Lead the architecture, design, and implementation of enterprise GenAI platforms and AI-powered applications.
- Design and develop Agentic AI workflows using autonomous and multi-agent frameworks.
- Build and optimize RAG (Retrieval-Augmented ) pipelines integrating enterprise knowledge sources.
- Develop intelligent workflow automation solutions using GenAI and AI agents.
- Collaborate with business stakeholders to identify AI opportunities, define use cases, and architect scalable solutions.
- Create AI solutions for:
- Business process automation
- Knowledge management
- Intelligent document processing
- Conversational AI
- Decision support systems
- Code and developer productivity
- Design scalable APIs and microservices for AI applications.
- Implement observability, monitoring, guardrails, security, and governance for AI systems.
- Optimize LLM performance, prompt engineering, latency, and cost efficiency.
- Mentor engineering teams and establish AI engineering best practices.
- Drive deployment automation and production readiness using DevOps and MLOps practices.
Required Skills & Experience
Generative AI & LLMs
- Strong experience with:
- OpenAI GPT models
- Claude
- Gemini
- Llama
- Mistral
- Expertise in:
- Prompt engineering
- Fine-tuning concepts
- Function calling / tool usage
- AI agent orchestration
- Context management
- Memory handling
Agentic AI & Workflow Orchestration
- Experience designing autonomous AI systems using:
- LangChain
- LangGraph
- CrewAI
- AutoGen
- Semantic Kernel
- LlamaIndex
- Strong understanding of:
- Multi-agent systems
- Planning and reasoning workflows
- Human-in-the-loop workflows
- AI orchestration patterns
RAG & Knowledge Systems
- Hands-on experience with:
- RAG architecture design
- Vector databases
- Embedding models
- Semantic search
- Hybrid search
- Chunking strategies
- Document ingestion pipelines
- Experience with vector databases such as:
- Pinecone
- Weaviate
- ChromaDB
- FAISS
- Milvus
- Elasticsearch/OpenSearch vector search
Backend & API Development
- Strong Python development experience.
- Experience building:
- REST APIs
- FastAPI / Flask services
- AI microservices
- Async processing pipelines
- Familiarity with API integration patterns and enterprise integrations.
Cloud, DevOps & Deployment
- Experience with:
- Docker
- Kubernetes
- Containerization
- CI/CD pipelines
- GitHub Actions / Jenkins / GitLab CI
- Terraform or Infrastructure as Code
- Cloud platform expertise in one or more:
- AWS
- Azure
- GCP
- Exposure to:
- MLOps
- Model deployment
- Monitoring and logging
- AI governance and security
Frontend & UI
- Working knowledge of:
- Angular
- TypeScript
- React (good to have)
- Ability to collaborate on AI-powered UI/UX workflows and conversational interfaces.
Data & Databases
- Experience with:
- SQL / NoSQL databases
- PostgreSQL
- MongoDB
- Redis
- Data pipelines
- ETL workflows
Good to Have
- Experience with AI copilots and enterprise assistants.
- Knowledge of MCP (Model Context Protocol).
- Experience with workflow tools like:
- Apache Airflow
- n8n
- Temporal
- Camunda
- Exposure to AI security, responsible AI, and governance frameworks.
- Experience in Capital Markets, Banking, Healthcare, Retail, or other enterprise domains.
- Familiarity with OCR, speech-to-text, and multimodal AI solutions.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or related field.
- 8+ years of software engineering experience with 3+ years in AI/GenAI architecture and solutioning.
- Strong communication and stakeholder management skills.
- Proven ability to lead technical teams and drive enterprise AI adoption.
Profile
- Strong problem solver who can convert ambiguous business challenges into scalable AI solutions.
- Ability to balance innovation with enterprise-grade architecture and operational excellence.
- Passion for emerging AI technologies and rapid experimentation.