AI Engineering Professional

2 weeks ago

Chennai, Tamil Nadu, India beBeeArtificialintelligence Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
AI Engineer RoleThis is a challenging and rewarding role for an experienced AI engineer who can design, develop, and deploy AI/ML solutions with a focus on Generative AI and LLMs.Key Responsibilities:
  • Fine-tune, prompt-engineer, and optimize large language models 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 Skills and 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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