Generative AI Engineer
2 weeks ago
Job Description: Generative AI Engineer
Position Overview
We are seeking a highly skilled
Generative AI Engineer
with expertise in
Agentic AI
,
LLMs (Large Language Models)
, and
Retrieval-Augmented Generation (RAG)
to design, build, and deploy AI-powered solutions. The ideal candidate will have 6+ years of experience in AI/ML with strong hands-on expertise in building scalable GenAI applications, integrating with enterprise systems, and optimizing model performance for real-world use cases.
Key Responsibilities
- Design and implement
Generative AI solutions
leveraging LLMs and Agentic AI frameworks. - Develop and optimize
RAG pipelines
to enhance accuracy and context relevance of AI applications. - Build, fine-tune, and deploy
LLM-based applications
for tasks such as text generation, summarization, Q&A, and conversational AI. - Architect
agentic workflows
enabling autonomous AI agents to plan, reason, and execute tasks. - Collaborate with product, data, and engineering teams to integrate AI models into scalable systems.
- Evaluate, benchmark, and optimize LLMs for performance, latency, and cost.
- Stay current with the latest advancements in Generative AI, multi-agent systems, and foundation models.
- Ensure adherence to responsible AI principles, security, and compliance guidelines.
Required Skills & Qualifications
- 6+ years of experience
in AI/ML engineering, with at least 2+ years in Generative AI/LLM development. - Strong proficiency in
LLM frameworks & APIs
(OpenAI, Anthropic, Hugging Face, Llama, Mistral, etc.). - Expertise in
RAG (Retrieval-Augmented Generation)
and vector databases (e.g., Pinecone, FAISS, Weaviate, Milvus). - Hands-on experience with
Agentic AI frameworks
(LangChain, LlamaIndex, AutoGen, Haystack Agents, or similar). - Proficiency in
Python
and ML libraries (PyTorch, TensorFlow, Transformers). - Experience with
cloud platforms
(AWS, Azure, GCP) and containerization (Docker, Kubernetes). - Strong background in
MLOps practices
– model deployment, monitoring, and optimization. - Knowledge of
prompt engineering, fine-tuning, and instruction tuning
techniques. - Excellent problem-solving and communication skills with ability to work in cross-functional teams.
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