AI Engineer

About the role

AI Engineer

Role Overview:

As an AI Engineer at Particle41 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.

In This Role, You Will:

- 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. LangChain, LangGraph, MCP Clients/Servers, Agents SDKs, LiveKit etc.) and propose innovative ideas.

- Work directly with clients to understand problem statements, translate business requirements into AI solutions, and clearly communicate results.

Skills and Experience We Value:

- 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 (TensorFlow, PyTorch, scikit-learn, LangChain, LangGraph).

- 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, ElevenLabs, Cartesia etc.

- Experience in computer vision, time-series modelling (ARIMA, Prophet) or multimodal AI.

- Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker).

- 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