SR AI Engineer
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Job Location:
Monthly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
Were hiring a Senior AI Engineer to lead the development
and deployment of cutting-edge AI
systems with a focus on large language models (LLMs)
real-time conversational agents
multimodal intelligence and multilingual capabilities. This
and deployment of cutting-edge AI
systems with a focus on large language models (LLMs)
real-time conversational agents
multimodal intelligence and multilingual capabilities. This
role bridges deep ML expertise system design and hands-
on implementation - ideal for someone excited to turn AI
on implementation - ideal for someone excited to turn AI
innovation into real world product impact. Youll work
across open-source and commercial models cloud
platforms and global user contexts to shape the future of
AI-driven user experiences.
*** This is a hybrid position - working 3 days a week from our
Bangalore office. ***
Key Responsibilities Key Responsibilities:
across open-source and commercial models cloud
platforms and global user contexts to shape the future of
AI-driven user experiences.
*** This is a hybrid position - working 3 days a week from our
Bangalore office. ***
Key Responsibilities Key Responsibilities:
LLM & Multimodal AI Development:
Design fine-tune and evaluate LLMs (e.g. LLaMA Mistral)
using modern training workflows (LoRA QLoRA DPO SFT).
Build and integrate AI systems that process and generate
across text audio images and structured data.
Engineer robust prompts and control flows for multilingual
compliance-sensitive and regulated domains.
Develop and maintain retrieval-augmented generation
(RAG) pipelines and vector databases to ground AI outputs
in enterprise data and conversational history.
Production-Grade Delivery & Cloud Deployment:
Deploy scalable secure ML systems on AWS Azure and
Google Cloud.
Translate prototypes into reliable cost-optimized
production systems.
Run A/B tests and integrate feedback loops into model
refinement cycles.
Design fine-tune and evaluate LLMs (e.g. LLaMA Mistral)
using modern training workflows (LoRA QLoRA DPO SFT).
Build and integrate AI systems that process and generate
across text audio images and structured data.
Engineer robust prompts and control flows for multilingual
compliance-sensitive and regulated domains.
Develop and maintain retrieval-augmented generation
(RAG) pipelines and vector databases to ground AI outputs
in enterprise data and conversational history.
Production-Grade Delivery & Cloud Deployment:
Deploy scalable secure ML systems on AWS Azure and
Google Cloud.
Translate prototypes into reliable cost-optimized
production systems.
Run A/B tests and integrate feedback loops into model
refinement cycles.
Evaluation Safety & Compliance:
Develop structured evaluation frameworks for accuracy
latency safety and compliance.
Calibrate models for predictable safe and compliant
behavior in production environments.
Collaboration Mentorship & Best Practices:
Partner with cross-functional teams to align AI solutions
with business needs.
Mentor junior engineers and champion MLOps/LLMOps best
practices.
Required Skills & Qualifications:
5 years of experience in ML/AI development and
deployment including 1 2 years working with LLMs or
Generative AI.
Hands-on experience with LLM architectures training
workflows and multimodal AI systems.
Proficient in prompt engineering for multilingual and
regulated domains.
Strong Python skills; familiarity with Hugging Face
LangChain or LlamaIndex vector databases and inference
tuning.
Experience working with conversational data annotation
pipelines and outcome-linked training sets.
Deep knowledge of ML tooling across AWS Azure and GCP.
MLOps expertise: containerization orchestration CI/CD
distributed inference and real-time ML systems.
Bachelors or masters degree in computer science
Machine Learning or a related field.
Ideal Candidate Traits:
Designs and builds end-to-end systems (model prompt
deployment).
Focused on real-world performance and measurable
impact.
Adapts quickly to evolving GenAI tools and frameworks.
Communicates technical concepts clearly to both technical
and non-technical stakeholders.
Enjoys building from scratch and thrives in ambiguity.
Develop structured evaluation frameworks for accuracy
latency safety and compliance.
Calibrate models for predictable safe and compliant
behavior in production environments.
Collaboration Mentorship & Best Practices:
Partner with cross-functional teams to align AI solutions
with business needs.
Mentor junior engineers and champion MLOps/LLMOps best
practices.
Required Skills & Qualifications:
5 years of experience in ML/AI development and
deployment including 1 2 years working with LLMs or
Generative AI.
Hands-on experience with LLM architectures training
workflows and multimodal AI systems.
Proficient in prompt engineering for multilingual and
regulated domains.
Strong Python skills; familiarity with Hugging Face
LangChain or LlamaIndex vector databases and inference
tuning.
Experience working with conversational data annotation
pipelines and outcome-linked training sets.
Deep knowledge of ML tooling across AWS Azure and GCP.
MLOps expertise: containerization orchestration CI/CD
distributed inference and real-time ML systems.
Bachelors or masters degree in computer science
Machine Learning or a related field.
Ideal Candidate Traits:
Designs and builds end-to-end systems (model prompt
deployment).
Focused on real-world performance and measurable
impact.
Adapts quickly to evolving GenAI tools and frameworks.
Communicates technical concepts clearly to both technical
and non-technical stakeholders.
Enjoys building from scratch and thrives in ambiguity.
#LI-RS
Function Product Delivery
Country India
Office Location Bangalore
Work Place type Hybrid
Country India
Office Location Bangalore
Work Place type Hybrid
Key Skills
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