Senior Machine Learning Engineer
5 days ago
We're Hiring: Senior ML Engineer (Gen AI, Machine Learning)
Mandatory Skills:
Graph ML, Agentic AI
Location:
Remote
Domain:
Generative AI | Machine Learning | Distributed Systems
Overview
Are you energized by the idea of innovating with
Generative AI
?
Do you want to build
next-gen AI-driven products with real-world global impact
?
This is a
greenfield opportunity
to architect and scale intelligent AI systems across
Generative AI, Machine Learning, Big Data, and Cloud Computing
. You'll contribute directly to the
vision, technical roadmap, and user experience
—from concept to production.
Our
AI Core Group
is pioneering intelligent platforms powered by:
AI Agents
RAG systems & Knowledge Bases
Graph ML
Data Mining & Anomaly Detection
LLM Fine-tuning & Optimization
We are redefining how enterprises leverage AI by building
multi-agent autonomous systems that perceive, reason, and act in real time.
Key Responsibilities
Act as an AI thought leader, driving strategic innovation across products and platforms
Lead the full SDLC — design ➝ testing ➝ deployment ➝ operations
Participate in architecture reviews, design reviews, code reviews & technical strategy
Build high-performance, production-grade ML systems for real-time AI platforms
Extend and optimize ML/GenAI libraries, frameworks & internal toolkits
Collaborate with AI scientists & platform engineers to accelerate experimentation & deployments
Deploy and scale ML solutions across cloud-based distributed systems
Requirements
Degree in Computer Science, Mathematics, or a related field
8+ years of experience
across the full SDLC (design → development → deployment → operations)
5+ years of building & deploying end-to-end ML solutions in production
Hands-on experience with
RAG, AI Agents, LLM Fine-tuning and Gen AI
in production
Experience with
AWS / Azure / GCP
& distributed systems
Proven ability to solve
complex & ambiguous problems
Preferred Qualifications
MS / PhD in CS, ML or related field
Expertise in
Graph ML & Graph technologies
(GNNs, GraphRAG, etc.)
Experience with distributed
Big Data ecosystems:
Spark
• Flink
• Kafka
• PySpark
• Lakehouse
• Druid
• Hudi
• Glue
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