
Generative AI Engineer
4 weeks ago
Software Developer
Key Responsibilities
- Design, develop, and deploy AI/ML solutions with a focus on Generative AI and LLMs.
- Fine-tune, prompt-engineer, and optimize LLMs for domain-specific tasks.
- Leverage frameworks such as LangChain, LlamaIndex, or similar for building modular AI applications.
- Integrate LLMs with APIs, databases, vector stores, and enterprise applications.
- Evaluate and implement model deployment strategies (on-prem, cloud, serverless, or containerized environments).
- Collaborate with cross-functional teams (data engineers, product managers, software engineers) to deliver end-to-end AI solutions.
- Stay updated with the latest advancements in AI, ML, and GenAI research and propose adoption of new technologies.
- Implement MLOps best practices for continuous training, deployment, and monitoring of models in production.
- Ensure responsible AI practices, focusing on bias mitigation, data privacy, and compliance.
Required Qualifications
- 5+ years of experience in software development (Python preferred; familiarity with Java, C#, or Node.js a plus).
- 2+ years of hands-on experience in Generative AI and LLM-based development.
- Strong knowledge of AI/ML fundamentals including NLP, deep learning, embeddings, and transformers.
- Experience with LLMs (e.g., OpenAI GPT, Anthropic Claude, LLaMA, Falcon, Mistral, or similar).
- Proven track record of delivering at least 2 production-grade LLM-based projects.
- Experience with LangChain, LlamaIndex, Haystack, or similar frameworks.
- Familiarity with vector databases (Pinecone, Weaviate, Milvus, FAISS, Chroma).
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and AI services (Bedrock, VertexAI, Azure OpenAI).
- Proficiency in containerization and deployment tools (Docker, Kubernetes, CI/CD pipelines).
- Experience with MLOps tools (MLflow, Kubeflow, Weights & Biases) for model lifecycle management.
- Strong problem-solving, analytical, and communication skills.
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