Staff Engineer, Machine Learning

Mumbai - India

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Department:

Engineering

Job Summary

REQUIREMENTS:

Total experience of 6 years
Strong expertise in Python and backend engineering with experience building scalable distributed microservices.
Hands-on experience designing and delivering end-to-end RAG (Retrieval-Augmented Generation) workflows in production systems.
Solid understanding of ML solution design including embeddings retrieval ranking feature engineering and evaluation strategies.
Experience with vector databases (FAISS Pinecone Milvus Weaviate) and implementing chunking indexing vector search re-ranking caching and memory patterns.
Knowledge of LLM/NLP engineering including prompt engineering model integration orchestration tools (LangChain/LlamaIndex) and evaluation instrumentation.
Experience productionizing ML systems with observability online/offline parity and performance optimization across latency throughput and cost.
Strong backend integration skills using REST/gRPC APIs Docker Kubernetes CI/CD and cloud platforms (AWS/GCP/Azure).
Ability to independently design ship and operate reliable scalable and cost-efficient ML-backed backend systems with strong ownership mindset.

RESPONSIBILITIES:

Design and build core backend services powering AI/ML runtime including orchestration session/state management and tools/services integration.
Implement end-to-end retrieval and memory systems covering ingestion embeddings indexing vector search ranking caching and lifecycle management.
Productionize ML workflows with feature/metadata services model integration contracts and evaluation hooks.
Drive performance reliability and cost optimization with strong SLO ownership and observability practices (logs metrics tracing guardrails).
Collaborate with applied ML teams on model routing prompts/tools evaluation datasets and safe releases.
Translate business requirements into scalable technical designs define NFR benchmarks and review architecture for extensibility and best practices.
Lead troubleshooting root-cause analysis and POCs to validate technology and design decisions.

Qualifications :

Bachelors or masters degree in computer science Information Technology or a related field.

Remote Work :

Employment Type :

Full-time

REQUIREMENTS:Total experience of 6 yearsStrong expertise in Python and backend engineering with experience building scalable distributed microservices.Hands-on experience designing and delivering end-to-end RAG (Retrieval-Augmented Generation) workflows in production systems.Solid understanding of M...

REQUIREMENTS:

Total experience of 6 years
Strong expertise in Python and backend engineering with experience building scalable distributed microservices.
Hands-on experience designing and delivering end-to-end RAG (Retrieval-Augmented Generation) workflows in production systems.
Solid understanding of ML solution design including embeddings retrieval ranking feature engineering and evaluation strategies.
Experience with vector databases (FAISS Pinecone Milvus Weaviate) and implementing chunking indexing vector search re-ranking caching and memory patterns.
Knowledge of LLM/NLP engineering including prompt engineering model integration orchestration tools (LangChain/LlamaIndex) and evaluation instrumentation.
Experience productionizing ML systems with observability online/offline parity and performance optimization across latency throughput and cost.
Strong backend integration skills using REST/gRPC APIs Docker Kubernetes CI/CD and cloud platforms (AWS/GCP/Azure).
Ability to independently design ship and operate reliable scalable and cost-efficient ML-backed backend systems with strong ownership mindset.

RESPONSIBILITIES:

Design and build core backend services powering AI/ML runtime including orchestration session/state management and tools/services integration.
Implement end-to-end retrieval and memory systems covering ingestion embeddings indexing vector search ranking caching and lifecycle management.
Productionize ML workflows with feature/metadata services model integration contracts and evaluation hooks.
Drive performance reliability and cost optimization with strong SLO ownership and observability practices (logs metrics tracing guardrails).
Collaborate with applied ML teams on model routing prompts/tools evaluation datasets and safe releases.
Translate business requirements into scalable technical designs define NFR benchmarks and review architecture for extensibility and best practices.
Lead troubleshooting root-cause analysis and POCs to validate technology and design decisions.

Qualifications :

Bachelors or masters degree in computer science Information Technology or a related field.

Remote Work :

Employment Type :

Full-time

Key Skills

Computer Science
Docker
Kubernetes
Python
VMware
C/C++
Go
System Architecture
gRPC
OS Kernels
Perl
Distributed Systems

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About Company

Nagarro

Nagarro helps future-proof your business through a forward-thinking, fluidic, and CARING mindset. We excel at digital engineering and help our clients become human-centric, digital-first organizations, augmenting their ability to be responsive, efficient, intimate, creative, and susta ... View more

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