AI/ML Engineers

6 days ago

Bengaluru, Karnataka, India Prana Tree Full time

Job Title: AI/ML Engineer
Experience:
4 – 6 Years

Work Mode:
Work from Office (WFO)

Locations:
Chennai | Bangalore | Hyderabad

Position Overview

We are seeking a highly skilled
AI/ML Engineer
responsible for designing, developing, and deploying scalable Machine Learning and Generative AI solutions. The ideal candidate will have strong expertise in data engineering, model lifecycle management, and modern AI frameworks — with a focus on production-grade AI/ML systems and automation through MLOps and LangOps practices.

Key Responsibility

AI/ML Development

  • Design, train, and optimize
    machine learning models
    for real-world enterprise applications.
  • Build and maintain
    end-to-end ML pipelines
    , including data pre-processing, feature engineering, model training, validation, and deployment.
  • Collaborate with
    data engineers and software developers
    to integrate ML models into production systems.
  • Monitor and maintain
    model performance
    , detect data drifts, and implement retraining strategies for continuous improvement.

Generative AI (GenAI)

Agentic Solution Design & Orchestration

  • Architect
    LLM-powered applications
    with dynamic intent routing and tool integration.
  • Develop
    agentic workflows
    using frameworks such as
    LangGraph
    or equivalents to ensure determinism, scalability, and guardrails.
  • Integrate
    MCP-compatible tools and services
    to extend GenAI capabilities.

Retrieval & Embeddings

  • Build and optimize
    RAG (Retrieval-Augmented Generation)
    systems using advanced embedding, chunking, and reranking strategies.
  • Enhance search efficiency through
    vector store optimization
    , hybrid search, and metadata schema design.

Prompting & Model Strategy

  • Design
    robust prompting frameworks
    and function-calling templates.
  • Evaluate
    generic vs. fine-tuned LLMs
    based on cost, latency, accuracy, and maintainability.

Data & Integrations

  • Implement
    NL2SQL
    and guarded SQL execution patterns.
  • Connect models with
    microservices and enterprise APIs
    for secure, real-time data access.
  • Define and maintain
    data schemas, metadata, and lineage
    for reliability and traceability.

Production Readiness

  • Establish
    evaluation datasets
    and
    automated regression tests
    for RAG and agentic solutions.
  • Monitor performance metrics — precision, recall, hallucination rate, latency, and cost.
  • Enforce
    data privacy, PII protection, and access control
    across deployments.

MLOps / LangOps

  • Manage
    versioning of prompts, models, and embeddings
    , and oversee controlled rollouts with A/B testing.
  • Implement
    monitoring, tracing, and telemetry
    for AI agents and tool invocations.
  • Define
    fallback, timeout, and retry policies
    to ensure system reliability and resiliency.

Qualifications

  • Programming:
    Strong proficiency in
    Python
    (NumPy, Pandas, Scikit-learn).
  • Frameworks:
    Hands-on experience with
    TensorFlow
    or
    PyTorch
    .
  • Machine Learning & Deep Learning:
    Experience with supervised, unsupervised, and reinforcement learning methods.
  • Data Management:
    Proficient with
    SQL/NoSQL databases
    , data preprocessing, and feature engineering.
  • MLOps:
    Exposure to
    CI/CD
    , pipeline automation, and model deployment workflows.

Preferred Skills

  • Experience with
    LangChain
    ,
    LangGraph
    , or other agentic AI frameworks.
  • Familiarity with
    vector databases
    and
    semantic search
    .
  • Exposure to
    cloud-based ML platforms
    such as
    AWS, Azure, or GCP
    .
  • Excellent
    problem-solving, communication, and collaboration
    skills.

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