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Accolite
Job Summary:
We are seeking 9+ years experienced AI Engineers with strong expertise in Python, Agentic AI frameworks, and hands-on experience working with Google Gemini models. The ideal candidate will build intelligent multi-agent workflows, develop LLM-powered applications, design RAG pipelines, and integrate AI capabilities into enterprise systems. This role requirements strong engineering skills, model integration experience, and the ability to build production-grade AI solutions.
Key Responsibilities:
Design and develop LLM-powered applications using Google Gemini models (Pro, Ultra, Flash, etc.).
Build agentic AI systems using frameworks like LangChain, CrewAI, AutoGen, Semantic Kernel, etc.
Develop RAG pipelines, embedding workflows, and vector search integrations.
Write clean, optimized Python code for orchestration, inference, and automation tasks.
Build scalable APIs and backend services to expose AI capabilities to enterprise applications.
Integrate Gemini models with cloud services (Vertex AI) and enterprise data sources.
Implement prompt engineering, chain-of-thought design, and multi-step agent reasoning.
Optimize model latency, cost, and throughput for production deployments.
Collaborate with product, data engineering, and platform teams to define AI solution architecture.
Ensure governance, security, and responsible AI practices.
Required Qualifications:
Strong hands-on experience in Python for AI/ML application development.
Experience building Agentic AI workflows with LangChain / AutoGen / CrewAI / similar frameworks.
Hands-on experience using Google Gemini models or Vertex AI LLMs.
Understanding of LLMs, embeddings, vector databases (FAISS, Pinecone, Chroma).
Experience building RAG, conversational AI, and LLM inference pipelines.
Experience integrating AI services via REST, gRPC, Web APIs.
Familiarity with cloud platforms (Google Cloud preferred; AWS/Azure acceptable).
Strong problem-solving and debugging skills.
Preferred Skills:
Experience with Vertex AI Pipelines or serverless AI deployment.
Familiarity with Docker/Kubernetes for containerized AI deployments.
Experience with observability for AI workloads (monitoring, tracing, logging).
Understanding of ML evaluation, safety, bias, and model governance.
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