
Visionary Generative AI Developer
1 week ago
You will play a pivotal role in shaping the future of Generative AI and Large Language Models.
- Key Responsibilities
- Design, develop, and deploy cutting-edge AI/ML solutions leveraging frameworks such as LangChain or similar for building modular AI applications.
- Fine-tune, prompt-engineer, and optimize LLMs for domain-specific tasks to achieve maximum accuracy and efficiency.
- Integrate LLMs with APIs, databases, vector stores, and enterprise applications to ensure seamless deployment and scalability.
- Evaluate and implement model deployment strategies (on-prem, cloud, serverless, or containerized environments) to meet business needs.
- Collaborate with cross-functional teams to deliver end-to-end AI solutions, staying updated with the latest advancements in AI, ML, and GenAI research.
- 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 to maintain a trustworthy AI ecosystem.
Required Skills
- 5+ years of experience in software development (Python preferred)
- 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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