Gen AI

Infogain

6 - 9 years

22 - 30 Lacs

bengaluru

Posted:None| Platform:

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Skills Required

gen ai python ml ops

Work Mode

Hybrid

Job Type

Full Time

Job Description

Job Summary:

Provides technical leadership and strategic direction for the AI/ML team. Designs and implements AI/ML solutions that meet business needs and drive innovation.

Key Responsibilities

Design, develop, and optimize
Machine Learning & Deep Learning models
using
Python
and libraries such as
TensorFlow, PyTorch, and Scikit-learn
Work with
Large Language Models
(e.g., GPT, BERT, T5) to solve NLP tasks such as, semantic search, summarization, chatbots, conversational agents, and document intelligence.
Lead the development of scalable AI solution including data preprocessing,
embedding generation, vector search, and prompt orchestration
.
Build and manage
vector databases and metadata stores
to support high-performance semantic retrieval and contextual memory.
Implement caching, queuing, and background processing systems to ensure performance and reliability at scale.
Conduct
independent R&D
to implement cutting-edge AI methodologies, evaluate open-source innovations, and prototype experimental solutions
Apply
predictive analytics
and statistical techniques to mine actionable insights from structured and unstructured data.
Build and maintain
robust data pipelines
and infrastructure for end-to-end ML model training, testing, and deployment
Collaborate with
cross-functional teams
to integrate AI solutions into business processes
Contribute to the
MLOps lifecycle
, including model versioning, CI/CD, performance monitoring, retraining strategies, and deployment automation
Stay updated with the
latest developments
in AI/ML by reading academic papers, and experimenting with novel tools or frameworks

Required Skills & Qualifications

Proficient in
Python
, with hands-on experience in key ML libraries:
TensorFlow, PyTorch, Scikit-learn, and HuggingFace Transformers
Strong understanding of
machine learning fundamentals
,
deep learning architectures
(CNNs, RNNs, transformers), and statistical modeling
Practical experience working with and fine-tuning
LLMs
and foundation models
Deep understanding of
vector search, embeddings, and semantic retrieval techniques.
Expertise in
predictive modeling
, including regression, classification, time series, clustering, and anomaly detection
Comfortable working with
large-scale datasets
using Pandas, NumPy, SciPy etc.
Experience with
cloud platforms
(AWS, GCP, or Azure) for training and deployment is a plus

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