What are the responsibilities and job description for the Lead Game AI Engineer position at Straive?
Job Description
We are seeking a highly technical, hands-on Sr. Gen AI Engineer / Lead Gen AI Engineer to drive the design, development, and deployment of production-grade Generative AI applications. In this dual-impact role, you will act as a primary individual contributor architecting core AI engines while simultaneously leading engineers through task allocation, code reviews, and technical mentorship. The ideal candidate bridges the gap between state-of-the-art AI research (LLMs, Agentic frameworks, Advanced RAG, OCR) and production-grade full-stack engineering (Python, FastAPI, React).
Key Responsibilities
Generative AI & Agentic Systems
: Architect, build, and optimize LLM-powered applications, multi-agent workflows (e.g., CrewAI, AutoGen, LangGraph), and autonomous AI agents.
RAG & Document Intelligence
: Design and deploy advanced RAG architectures and document processing pipelines utilizing OCR techniques (e.g., LayoutLM, PaddleOCR, Tesseract, Azure Document Intelligence) and Vision LLMs to extract structured data.
Backend Microservices
: Build robust, asynchronous, high-throughput microservices and RESTful APIs using Python and FastAPI.
Frontend Integration
: Collaborate on or build modern web interfaces using React (e.g., Control Towers, operations dashboards, interactive chat interfaces).
MLOps & Infrastructure
: Oversee model deployment, prompt engineering, fine-tuning, vector database integration (Pinecone, Qdrant, Chroma, PGVector), and cloud infrastructure setup (Azure/AWS).
Technical Oversight
: Lead a team of AI, backend, and full-stack engineers; allocate tasks, establish sprint priorities, and ensure timely delivery.
Conduct rigorous code reviews
: To maintain high engineering standards, security, performance, and scalability across AI and full-stack codebases.
Key Qualifications & Skills
Overall Experience
: 6 to 12 years of professional software engineering experience.
AI/ML Domain Experience
: 3 years of dedicated, hands-on experience building and deploying AI/ML, OCR, and Generative AI solutions in production.
Core Technical Stack
:
Generative AI & LLMs: Extensive experience with commercial and open-source LLMs (OpenAI, Anthropic Claude, Llama), Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI), and LLM evaluation frameworks (LangSmith, TruLens, RAGAS).
RAG & Unstructured Data: Strong knowledge of hybrid search, re-ranking, chunking strategies, vector databases, and document intelligence workflows.
OCR & Vision Techniques: Hands-on experience with OCR engines (Tesseract, PaddleOCR, Azure Document Intelligence) and Multi-Modal/Vision LLMs.
Backend: Deep expertise in Python and asynchronous frameworks (FastAPI, AsyncIO).
Frontend: Working proficiency in React (TypeScript/JavaScript) for building interactive web UI components.
Cloud & DevOps: Hands-on experience with cloud platforms (Azure / AWS), Docker, Kubernetes, and CI/CD pipelines.
We are seeking a highly technical, hands-on Sr. Gen AI Engineer / Lead Gen AI Engineer to drive the design, development, and deployment of production-grade Generative AI applications. In this dual-impact role, you will act as a primary individual contributor architecting core AI engines while simultaneously leading engineers through task allocation, code reviews, and technical mentorship. The ideal candidate bridges the gap between state-of-the-art AI research (LLMs, Agentic frameworks, Advanced RAG, OCR) and production-grade full-stack engineering (Python, FastAPI, React).
Key Responsibilities
Generative AI & Agentic Systems
: Architect, build, and optimize LLM-powered applications, multi-agent workflows (e.g., CrewAI, AutoGen, LangGraph), and autonomous AI agents.
RAG & Document Intelligence
: Design and deploy advanced RAG architectures and document processing pipelines utilizing OCR techniques (e.g., LayoutLM, PaddleOCR, Tesseract, Azure Document Intelligence) and Vision LLMs to extract structured data.
Backend Microservices
: Build robust, asynchronous, high-throughput microservices and RESTful APIs using Python and FastAPI.
Frontend Integration
: Collaborate on or build modern web interfaces using React (e.g., Control Towers, operations dashboards, interactive chat interfaces).
MLOps & Infrastructure
: Oversee model deployment, prompt engineering, fine-tuning, vector database integration (Pinecone, Qdrant, Chroma, PGVector), and cloud infrastructure setup (Azure/AWS).
Technical Oversight
: Lead a team of AI, backend, and full-stack engineers; allocate tasks, establish sprint priorities, and ensure timely delivery.
Conduct rigorous code reviews
: To maintain high engineering standards, security, performance, and scalability across AI and full-stack codebases.
Key Qualifications & Skills
Overall Experience
: 6 to 12 years of professional software engineering experience.
AI/ML Domain Experience
: 3 years of dedicated, hands-on experience building and deploying AI/ML, OCR, and Generative AI solutions in production.
Core Technical Stack
:
Generative AI & LLMs: Extensive experience with commercial and open-source LLMs (OpenAI, Anthropic Claude, Llama), Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI), and LLM evaluation frameworks (LangSmith, TruLens, RAGAS).
RAG & Unstructured Data: Strong knowledge of hybrid search, re-ranking, chunking strategies, vector databases, and document intelligence workflows.
OCR & Vision Techniques: Hands-on experience with OCR engines (Tesseract, PaddleOCR, Azure Document Intelligence) and Multi-Modal/Vision LLMs.
Backend: Deep expertise in Python and asynchronous frameworks (FastAPI, AsyncIO).
Frontend: Working proficiency in React (TypeScript/JavaScript) for building interactive web UI components.
Cloud & DevOps: Hands-on experience with cloud platforms (Azure / AWS), Docker, Kubernetes, and CI/CD pipelines.