AI Engineer

Role Overview:

As an AI Engineer at Particle
41 you will design, develop and deploy machine-learning and deep-learning models/solutions (including generative-AI) that address complex business problems. You will collaborate with data scientists, data engineers, software engineers and client stakeholders. You will also build scalable pipelines, ensure production readiness, monitor performance and iterate proactively.

• Lead end-to-end development of AI/ML models: from data ingestion & preprocessing to model training, evaluation, deployment and monitoring.
• Build generative-AI solutions (RAG, Agentic Workflows, MCP Servers, Conversation AI Agents) aligned with business goals.
• Work closely with data engineering teams to build/maintain data pipelines, feature stores and orchestration frameworks.
• Develop and integrate AI models into production systems (APIs, microservices, cloud deployments).
• Optimize models/solutions for performance, scalability, robustness and cost-efficiency.
• Monitor models/solutions' performance in production (drift, bias, fairness, reliability) and implement remediation.
• Document model design, experiments, data provenance and solution rationale.
• Stay abreast of latest AI/ML research, frameworks, tools (e.g. Lang Chain, Lang Graph, MCP Clients/Servers, Agents SDKs, Live Kit etc.) and propose innovative ideas.
• Work directly with clients to understand problem statements, translate business requirements into AI solutions, and clearly communicate results.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
• 3+ years of hands‑on experience in AI/ML model development and deployment.
• Strong programming skills in Python and experience with major ML/AI frameworks (
  Tensor Flow, PyTorch, scikit-learn, Lang Chain, Lang Graph
  ).
• Experience with vector databases (e.g.,
  Pinecone, FAISS
  ),
  RAG and Agentic workflows
  .
• Experience building or fine-tuning Large Language Models (LLMs).
• Experience deploying models into production: building REST APIs, microservices and monitoring.
• Familiarity with Text-to-Speech and Speech-to-Text models/solutions like Deepgram, Eleven Labs, Cartesia etc.
• Experience in computer vision, time-series modelling (
  ARIMA, Prophet
  ) or multimodal AI.
• Familiarity with MLOps tools and frameworks (e.g.,
  MLflow, Kubeflow, Sage Maker
  ).
• Strong understanding of algorithms, data structures, statistics, and machine-learning fundamentals (classification, regression, clustering, deep learning, sequence models).
• Ability to work in a fast-paced, dynamic environment and adapt to changing priorities.
• Publications, open-source contributions, or personal projects in the AI/ML domain.