AI Engineer (2–3 yrs)

As our AI Engineer, you’ll own the design, development, and production-grade deployment of our machine learning and NLP pipelines. You’ll work cross-functionally with backend (Java/Spring Boot), data (Kafka/MongoDB/ES), and frontend (React) teams to embed AI capabilities throughout.

Responsibilities

Build & Deploy ML/NLP Models

• Design end-to-end ML pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation and monitoring.
• Train, deploy and operate predictive models (classification, regression, anomaly detection) to drive actionable insights across all MCP sources.
• Implement NLP components - such as text classification, summarization, and conversational interfaces—to enhance chat-driven workflows and knowledge retrieval.

Data Engineering & Integration

•  Ingest, clean, and normalize data from Kafka/Mongo and third-party APIs
•  Define and maintain JSON-schema validations and transformation logic
•  Collaborate with backend services to embed AI outputs

Platform & Service Collaboration

•  Work with Java/Spring Boot teams to wrap models as REST endpoints or Kafka stream processors
•  Ensure end-to-end monitoring, logging, and performance tuning within Kubernetes
•  Partner with frontend engineers to surface AI insights in React-based chat interfaces

Continuous Improvement

•  Establish A/B testing, metrics, and feedback loops to tune model accuracy and latency
•  Stay on top of LLM and MLops best practices to evolve our AI stack

Qualifications

• Experience:

  2–3 years in ML/AI or data science roles, preferably in SaaS.

• Languages & Frameworks:

  Python and Familiarity with Java & Spring Boot for service integrations.

• Data & Infrastructure:

  Hands-on with Kafka, MongoDB, Redis or similar. Experience containerizing in Docker and deploying on Kubernetes. JSON-path/JSONLogic or similar transformation engines.

• Soft Skills:

  Excellent communication - able to translate complex AI concepts to product and customer teams.

Nice-to-Haves

• Experience integrating LLMs or building vector search indexes for semantic retrieval
• Prior work on chatbots or conversational UIs
• Familiarity with DevOps stack. (AWS/Azure, k8s, GitOps, Security, Observability and Incident management)