What You'll Do

? Define technical vision and architecture: Lead the design and architecture of scalable, production-grade GenAI systems using LangChain, LangGraph, FastAPI, and modern AI frameworks. Make critical technical decisions that balance innovation, scalability, and maintainability

? Drive GenAI innovation: Research, prototype, and implement cutting-edge GenAI techniques including advanced prompt engineering, RAG optimization, agentic AI systems, fine-tuning, and multi-agent orchestration

? Lead AI experimentation and evaluation: Design and implement comprehensive evaluation frameworks for LLM performance, establish best practices for prompt engineering, and drive data-driven decision making through rigorous experimentation

? Own GenAI at utility and program scale: Architect and optimize LLM workloads that support millions of calls across multiple utilities and channels, under strict cost and latency targets.

? Architect event-driven AI systems: Design and implement sophisticated event-driven architectures using SQS, asynchronous workflows, distributed processing, and microservices patterns

? Establish GenAI best practices: Define and enforce coding standards, design patterns, testing strategies, and operational excellence for GenAI applications. Champion observability, monitoring, and reliability

? Technical leadership and mentorship: Mentor and guide engineers across all levels, conduct architecture reviews, provide technical direction, and foster a culture of continuous learning and innovation

? Cross-functional collaboration: Partner with product management, data science, engineering leadership, and business stakeholders to translate business requirements into technical solutions. Communicate complex technical concepts to diverse audiences

? Drive strategic initiatives: Identify opportunities for AI-driven innovation, evaluate emerging technologies, and lead proof-of-concept projects that align with business objectives ? Ensure production excellence: Establish SLAs, implement monitoring and alerting, optimize performance and cost, and ensure high availability of AI services

? Thought leadership: Represent Bidgely in the GenAI community through technical blogs, conference talks, open-source contributions, and industry engagement

What Makes You Successful

? Education: BS/MS+ in Computer Science, AI/ML, or equivalent from premier institutes

? Experience: 5+ years of software engineering experience with at least 1-2 years focused on Generative AI, Agentic AI, and production LLM-based applications

? Expert-level knowledge of LangChain, LangGraph, and LLM orchestration frameworks; deep understanding of prompt engineering, RAG architectures, agentic AI, and multi-agent systems

? Production AI Systems: Proven track record of architecting and deploying production-grade AI systems at scale, handling millions of requests and complex workflows

? API & Microservices: Extensive experience building scalable RESTful APIs, microservices architectures, and event-driven systems using FastAPI, Flask, or similar frameworks ? LLM Integration: Deep expertise integrating and optimizing LLM providers; understanding of model selection, cost optimization, and latency management

? Technical Leadership: Demonstrated ability to lead technical teams, drive architectural decisions, and mentor engineers; experience with code reviews, technical design documents, and cross-team collaboration

? Problem Solving: Exceptional problem-solving skills with ability to tackle ambiguous, complex challenges; strong in algorithms, data structures, and system design

? Communication: Outstanding communication skills; ability to influence technical direction, present to leadership, and collaborate effectively across global distributed teams

Preferred Qualifications

? Python & Architecture: Expert-level Python programming with deep knowledge of software architecture, design patterns, distributed systems, and performance optimization

? Experience with vector databases (ChromaDB, Pinecone, Weaviate, FAISS) and advanced retrieval techniques

? Deep knowledge of LLM observability tools (LangSmith, Langfuse, Weights & Biases) and evaluation frameworks

? Expertise in AWS services (Bedrock, SQS, S3, Lambda, ECS, Parameter Store) and cloud-native architectures

? Experience with fine-tuning, RLHF, prompt optimization techniques, and model evaluation methodologies

? Knowledge of PostgreSQL optimization, Redis caching strategies, and database performance tuning

? Experience with Docker, Kubernetes, Terraform, and infrastructure-as-code

? Background in building conversational AI, chatbots, or virtual assistants

? Experience with MLOps, model deployment pipelines, and A/B testing frameworks

? Familiarity with emerging AI trends: multi-modal models, reasoning models, agent frameworks

Interested candidates can share their resume with : [HIDDEN TEXT]

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