AI/ML & Forward Deployed Engineer

Accountabilities

• • Design, develop, and deploy machine learning, AI, and GenAI solutions from proof of concept through production.
  • Build and optimize ML models and applications covering deep learning, NLP, forecasting, classification, regression, and anomaly detection use cases.
  • Develop production-grade GenAI applications using LLMs, RAG pipelines, embeddings, retrieval optimization, reranking, and prompt engineering.
  • Design and implement AI evaluation frameworks to assess model quality, reliability, relevance, and performance.
  • Build scalable AI services and integrations using REST and gRPC APIs as well as event-driven architectures.
  • Establish and maintain MLOps and LLMOps practices covering deployment, automation, versioning, monitoring, and lifecycle management.
  • Containerize and orchestrate AI applications using Docker and Kubernetes and integrate them into robust CI/CD pipelines.
  • Implement model monitoring, drift detection, performance tracking, and processes for continuous model improvement.
  • Ensure AI solutions meet enterprise requirements for data quality, governance, security, role-based access control, encryption, and auditability.
  • Collaborate with business and technical stakeholders to understand requirements, identify opportunities for AI adoption, and translate them into effective technical solutions.
  • Support solutions through production operations, troubleshooting, optimization, and ongoing improvements.
  • Apply strong engineering practices to ensure AI systems are scalable, secure, observable, maintainable, and aligned with business objectives.

  Requirements

  • 8+ years of professional software engineering or technical engineering experience.
  • Strong hands-on experience in Machine Learning and AI/ML Engineering.
  • Advanced Python development skills and practical experience with deep learning and machine learning techniques.
  • Experience with NLP, forecasting, classification, regression, and anomaly detection.
  • Proven experience building GenAI applications using LLMs and Retrieval-Augmented Generation (RAG) architectures.
  • Strong understanding of embeddings, retrieval tuning, reranking, prompt engineering, and AI/LLM evaluation methodologies.
  • Solid knowledge of MLOps and LLMOps principles and practices across the AI development lifecycle.
  • Hands-on experience with Docker, Kubernetes, and CI/CD technologies for production deployments.
  • Experience designing and developing REST and gRPC APIs and event-driven services.
  • Knowledge of model monitoring, model versioning, drift detection, performance evaluation, and model lifecycle management.
  • Strong understanding of data quality, data governance, security controls, RBAC, encryption, and audit trails.
  • Ability to work effectively with both technical and non-technical stakeholders and translate business challenges into practical AI solutions.
  • Strong problem-solving, analytical, communication, and collaboration skills.
  • Ability to operate effectively in fast-moving environments while balancing experimentation with production reliability.

  Benefits

  • Competitive annual salary of $100,000–$120,000.
  • Full-time opportunity with a remote working model.
  • Opportunity to work on cutting-edge AI, machine learning, and GenAI solutions.
  • Hands-on exposure to LLMs, RAG, MLOps, LLMOps, Kubernetes, and cloud-native engineering practices.
  • Opportunity to contribute to AI solutions from concept and experimentation through production deployment.
  • Work on technically challenging projects with a strong focus on scalability, security, governance, and observability.
  • Collaborative environment with opportunities to engage directly with stakeholders and influence AI solution strategy.
  • Opportunity to expand expertise across modern AI engineering, machine learning, and production software development.