AI Engineer

REQUIREMENT

Role: AI Engineer

Location: Jerseycity, NJ - Onsite

Fulltime

interview Process (3 Levels):

1.          L1 – Client Internal Panel (Teams/Zoom)

2.          Karat Interview – In Person at Client Office

3.          Discussion with Project Team (Teams/Zoom)

JD

·     10 years of relevant experience in Apps Development or systems analysis role

·       Core AI/ML Foundations:

o   Strong foundational knowledge in GenAI , Machine Learning (ML modeling), Data Science, Statistics, and AI fundamentals, including Natural Language Processing (NLP), Neural Networks, and Large Language Models (LLMs).

·       Generative AI & LLM Expertise:

o   Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source LLMs.

o   Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.

o   Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.

o   Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.

o   Hands-on experience with agentic framework-based use case implementation.

o   Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.

·       Programming & Data Engineering:

o   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.

o   Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.

o   Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.

o   Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.

·       Deployment & MLOps:

o   Critical: Hands-on experience deploying GenAI-based models to production environments.

o   Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.

o   Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.

·       Cloud & Containerization:

o   Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environment.

·       Soft Skills:

o   Strong problem-solving abilities, excellent collaboration skills for working effectively with cross-functional teams, and the capability to work independently on complex, ambiguous problems.