Aiengineer

Job Description : 
6-10 years of relevant experience in Apps Development or systems analysis role
Core AI/ML Foundations:
Strong foundational knowledge in hashtag#GenAI , hashtag#Machine Learning (ML modeling), hashtag#DataScience, Statistics, and hashtag#AI fundamentals, including hashtag#Natural Language Processing (hashtag#NLP), Neural Networks, and Large Language Models (hashtag#LLMs).
hashtag#GenerativeAI & hashtag#LLM Expertise:
Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source hashtag#LLMs.
Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.
Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.
Hands-on experience with agentic framework-based use case implementation.
Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
Programming & Data Engineering:
Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
Deployment & MLOps:
Critical: Hands-on experience deploying GenAI-based models to production environments.
Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.
Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.
Cloud & Containerization:
Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environment.
Soft Skills:
Strong problem-solving abilities, excellent collaboration skills for working effectively with cross-functional teams, and the capability to work independently on complex, ambiguous problems.