AI & Machine Learning Engineer / AI Solutions Architect

V-Work Infotech Solutions INC
United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Artificial Intelligence Airflow Amazon Web Services Computer Vision Microsoft Azure Cloud Computing Cloud Engineering Databases Continuous Integration Information Engineering DevOps
+47 more
Github Python (Programming Language) Knowledge Management PostgreSQL Machine Learning MongoDB MySQL Natural Language Processing OpenCV Open Source Technology Performance Tuning Recommender Systems Redis Tensorflow Standard Sql Azure Machine Learning Reinforcement Learning Google Cloud Enterprise Software Applications Chatbots Data Ingestion Pytorch Transfer Learning Large Language Models Snowflake Multi-Agent Systems Prompt Engineering Apache Spark IT Architecture Deep Learning Generative AI Keras Git Build Management Data Lakes AI Platforms Scikit Learn Kubernetes Information Technology HuggingFace Apache Kafka Machine Learning Operations Virtual Agents Terraform Docker Jenkins Databricks

Job description

We are seeking a highly experienced Senior AI & Machine Learning Engineer / AI Solutions Architect with 10-15+ years of IT experience to design, build, and deploy enterprise-scale AI and Machine Learning solutions. The ideal candidate will have strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, MLOps, Deep Learning, NLP, Computer Vision, and cloud-based AI platforms., * Design and develop enterprise AI/ML applications and intelligent automation solutions.

  • Build, fine-tune, and optimize Large Language Models (LLMs) and foundation models.
  • Develop Retrieval-Augmented Generation (RAG) and AI Agent solutions.
  • Design scalable AI architectures for enterprise applications.
  • Build and deploy end-to-end ML pipelines from data ingestion to production.
  • Develop AI-powered chatbots, copilots, recommendation engines, and predictive analytics solutions.
  • Implement prompt engineering and model optimization techniques.
  • Deploy AI solutions using MLOps best practices.
  • Monitor, evaluate, and improve AI model performance in production.
  • Collaborate with Data Engineers, Data Scientists, Software Engineers, and business stakeholders.
  • Implement Responsible AI, governance, security, and compliance standards.
  • Mentor engineering teams and provide technical leadership., * MLflow
  • Kubeflow
  • Azure Machine Learning
  • AWS SageMaker
  • Google Vertex AI
  • Docker
  • Kubernetes
  • CI/CD for ML Pipelines

Vector Databases

  • Pinecone
  • ChromaDB
  • FAISS
  • Weaviate
  • Milvus

Data Engineering

  • Apache Spark
  • Databricks
  • Snowflake
  • Apache Airflow
  • Kafka
  • Delta Lake

Cloud Platforms

  • AWS
  • Microsoft Azure
  • Google Cloud Platform (Google Cloud Platform)

Databases

  • PostgreSQL
  • MongoDB
  • MySQL
  • Redis

DevOps & Tools

  • Git
  • GitHub
  • Azure DevOps
  • Jenkins
  • Terraform

Requirements

  • Python (Expert)
  • SQL
  • Java
  • Scala

Artificial Intelligence & Machine Learning

  • Machine Learning
  • Deep Learning
  • Generative AI
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • Computer Vision
  • Reinforcement Learning
  • Transfer Learning
  • Fine-Tuning
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • AI Agents / Agentic AI
  • Multi-Agent Systems

AI Frameworks & Libraries

  • PyTorch
  • TensorFlow
  • Keras
  • Scikit-learn
  • Hugging Face Transformers
  • LangChain
  • LlamaIndex
  • DSPy
  • OpenCV

LLM Platforms

  • OpenAI
  • Azure OpenAI
  • Anthropic Claude
  • Google Gemini
  • Meta Llama
  • Mistral AI
  • Cohere, * Bachelor’’'’s or Master’’'’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  • 10-15+ years of IT experience, including 5+ years in AI/ML or Generative AI.
  • Strong expertise in enterprise AI architecture and ML model deployment.
  • Experience building production-grade AI and LLM applications.
  • Strong knowledge of cloud-native AI services and MLOps.
  • Excellent analytical, communication, and leadership skills. __________________

Preferred Experience

  • Enterprise AI Copilots and conversational AI.
  • RAG-based knowledge management systems.
  • AI Agents and workflow automation.
  • Fine-tuning open-source LLMs (Llama, Mistral, Falcon).
  • Healthcare, Banking, Insurance, Retail, Manufacturing, or Telecom domains.
  • AI governance, security, and Responsible AI frameworks., * Microsoft Certified: Azure AI Engineer Associate (AI-102)
  • AWS Certified Machine Learning - Specialty
  • Google Professional Machine Learning Engineer
  • Databricks Certified Machine Learning Professional
  • NVIDIA Deep Learning Institute Certifications

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