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AI ML Engineer

Opulentsoft
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Role: AI ML Engineer
Location: Remote

Position Description:
We are seeking a highly skilled and experienced AI/ML Developer to join our advanced AI engineering team. As a key contributor, you will be responsible for designing, building, and deploying production-grade machine learning models and AI-based applications across a wide range of domains, including generative AI, multimodal systems, and agentic architectures.

This role demands deep expertise in MLOps, AIOps, and end-to-end ML pipelines, along with practical experience in state-of-the-art LLMs, transformers, and modern AI frameworks. You will also work extensively on agent-based AI systems, multi-agent coordination, and autonomous AI agents using tools like LangChain, LangGraph, CrewAI, and AgentCore.

Key Responsibilities:

Model Development & Engineering

  • Design, train, fine-tune, and deploy ML and deep learning models using PyTorch, TensorFlow, and transformers.
  • Build and optimize LLM-based applications using frameworks like HuggingFace, LangChain, LangGraph, and CrewAI.
  • Implement prompt engineering and context engineering strategies for retrieval-augmented generation (RAG) and agentic tasks.
  • Work with multimodal models integrating text, image, audio, and video inputs.

AI Infrastructure & MLOps

  • Develop scalable MLOps pipelines using DVC, MLflow, model registries, CI/CD, monitoring, and automated retraining workflows.
  • Deploy and manage models on Kubernetes with HPA, Service Mesh (e.g., Istio/Linkerd), and ExternalDNS configurations.
  • Establish robust model endpoints, versioning, and rollback strategies.
  • Integrate model guardrails, Responsible AI principles, and bias/fairness mitigation into the model lifecycle.

AI Agents & Multi-Agent Systems

  • Develop autonomous AI agents, multi-agent collaboration systems, and agent-to-agent protocols using AgentCore, CrewAI, and LangGraph.
  • Architect and implement agentic AI protocols such as MCP (Multi-Agent Communication Protocols) and task delegation systems.
  • Create intelligent decision-making agents powered by both rule-based systems and LLMs.

Generative AI (GenAI) & RAG Systems

  • Build and fine-tune GenAI models using open-source and proprietary LLMs (e.g., GPT, LLaMA, Claude, Mistral, Falcon).
  • Develop RAG pipelines integrated with vector stores, context retrievers, and prompt optimizers.
  • Build scalable chatbots, copilots, AI assistants, and domain-specific agents.

ML Algorithms & Patterns

  • Implement and optimize classical and modern ML/DL algorithms for classification, regression, clustering, recommendation, time-series forecasting, etc.
  • Utilize ML design patterns for data-centric AI, model-centric AI, and deployment-centric AI practices.

Required Qualifications:
Core Technical Expertise

  • 5+ years in AI/ML development, with a strong portfolio of production-grade models.
  • Proficiency in Python, PyTorch, TensorFlow, Scikit-learn, and HuggingFace Transformers.
  • Experience with LangChain, LangGraph, CrewAI, AgentCore, and LLM orchestration frameworks.
  • Knowledge of RAG, embedding models, vector databases (Pinecone, FAISS, Weaviate, Qdrant).
  • Deep understanding of transformer architectures, prompt tuning, LoRA, PEFT, adapter tuning.
  • Expertise in MLOps/AIOps: MLflow, DVC, model registry, CI/CD, observability, Seldon/KServe.
  • Experience with cloud platforms (AWS, GCP, Azure), Kubernetes, Helm, and Terraform.

Systems & Infrastructure

  • Production experience with containerization (Docker) and orchestration (Kubernetes).
  • Knowledge of Service Mesh, ExternalDNS, Load Balancing, Auto Scaling (HPA).
  • Hands-on with distributed systems, streaming data, batch processing, and low-latency inference.

Agentic & Multimodal AI

  • Practical knowledge in autonomous agents, multi-agent collaboration, and agent-to-agent communication protocols.
  • Experience building multimodal AI applications combining text, image, speech, and structured data.

Responsible AI & Governance

  • Familiarity with model interpretability, ethical AI, privacy-preserving ML, guardrails, and governance policies.

Preferred Qualifications:

  • Experience with LLMOps platforms like BentoML, Baseten, or Modal.
  • Contribution to open-source AI projects or published research in ML/AI.
  • Understanding of neurosymbolic AI, causal inference, or neural architecture search (NAS).
  • Familiarity with knowledge graphs, ontologies, and semantic reasoning.