AI/ML Engineers
21 hours ago
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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