Senior AI Engineer

Hi

We are hiring Sr. AI Engineer & AI/ML+ Graph Engineer for one of our direct client in Bangalore!!

Job Title: Sr. AI Engineer

Location: Bangalore (Hybrid mode- 3 days from Office)

Experience: 6+ yrs

About the Role

We are seeking a Senior AI Engineer to lead the architecture, evaluation, and large-scale deployment of advanced Agentic AI systems, including:

• Multi-agent LLM systems

• Retrieval-Augmented Generation (RAG) platforms

• Hybrid ML + GenAI systems

• Enterprise-grade intelligent automation platforms

This is a senior technical leadership role requiring deep research expertise, strong architectural vision, and production engineering excellence.

You will define the technical direction of AI systems across the organization and shape how agentic AI is deployed at enterprise scale.

Role Overview

As a Senior AI Engineer, you will:

• Architect complex multi-agent AI systems

• Define LLM evaluation and reliability frameworks

• Lead advanced Agentic AI, GenAI and ML initiatives

• Establish AI engineering standards across teams

• Own scalability, robustness, and governance of AI deployments

• Partner with executive leadership on AI strategy and innovation roadmap

This is a high-ownership, high-influence role.

Core Responsibilities

Multi-Agent Systems Architecture

Design and implement multi-agent LLM orchestration frameworks.

Architect:

• Planner-Executor models

• Tool-using agents

• Memory-enabled agents

• Hierarchical and collaborative agent systems

• Define inter-agent communication protocols

• Implement structured reasoning pipelines

• Optimize token efficiency, latency, and throughput

• Ensure resilience and failover strategies in agent workflows

LLM Systems & RAG Architecture

Design scalable RAG systems including:

• Embedding strategy

• Intelligent chunking frameworks

• Retrieval optimization

• Hybrid search architectures

• Implement fine-tuning and instruction tuning strategies

• Architect hallucination mitigation mechanisms

• Establish prompt versioning and governance standards

• Optimize inference cost and performance at scale

LLM Evaluation Science & Reliability Engineering

Design evaluation frameworks for:

• Hallucination detection

• Groundedness scoring

• Faithfulness assessment

• Response quality benchmarking

• Implement automated LLM evaluation pipelines

• Develop synthetic dataset generation systems

• Design human-in-the-loop evaluation workflows

• Establish model drift monitoring and agent failure detection

• Build observability dashboards for AI behavior and reliability

• Define enterprise AI governance standards

Machine Learning & Predictive Systems

Lead development of:

• Classification and regression systems

• Deep learning architectures

• Anomaly detection systems

• Knowledge graph reasoning engines

• Define experimentation frameworks and statistical rigor

• Oversee model validation and optimization strategies

Production AI & Infrastructure

Architect AI systems for enterprise-grade production deployment. Define MLOps and LLMOps pipelines. Deploy systems using:

• AWS / GCP / Azure

• Docker / Kubernetes

• CI/CD pipelines

• Implement monitoring, logging, and observability

• Ensure scalability for high-volume AI workloads

• Define cost governance and resource optimization strategies

Technical Leadership & Strategic Influence

• Serve as architectural authority for AI systems

• Mentor senior AI engineers and data scientists

• Conduct research and architecture reviews

• Translate complex business challenges into scalable AI frameworks

• Drive long-term AI innovation roadmap in collaboration with leadership

Required Qualifications

• Bachelor's or Master's or PhD in Artificial Intelligence, Machine Learning, Computer Science, or related field

• 6+ years of experience in AI/ML engineering

• 3+ years leading complex AI initiatives

• Strong proficiency in Python

• Deep expertise in Machine Learning algorithms, Deep Learning architectures, and Transformer models

• Statistical modeling

• LangGraph is Must

• Proven track record of deploying AI systems into production

Preferred Qualifications

• Experience building production-grade multi-agent systems

• Expertise in LLM orchestration frameworks (LangGraph, LangChain, etc.)

• Experience designing and scaling RAG pipelines

• Experience fine-tuning large language models

• Knowledge graph reasoning expertise

• Experience with vector databases (Pinecone, Weaviate, etc.)

• Experience with speech AI systems (STT/TTS)

• Exposure to distributed systems architecture

What We're Looking For

• Systems-level architectural thinker

• Research depth with production pragmatism

• Strong mathematical and statistical foundation

• Exceptional communicator across technical and executive audiences

• High ownership mindset

• Commitment to scalable, measurable AI impact

Success Metrics

• Multi-agent systems successfully deployed in production

• LLM evaluation framework institutionalized across teams

• Measurable ROI delivered through AI initiatives

• Reduced hallucination rates and improved reliability

• Scalable and cost-efficient AI infrastructure

Job Title: AI/ML + Graph Engineer

Location: Bangalore (Hybrid mode- 3 days from Office)

Experience: 5+ yrs

About the role:

• This role will work closely on the core TAOS / V3 engine, focusing on graph-driven AI systems (rather than standard GenAI / RAG work).

• The ideal candidate should have a strong foundation in graph theory, knowledge graphs/ontologies, and graph algorithms, along with experience in traditional AI/ML techniques such as reinforcement learning or policy learning.

• Hands-on experience in fine-tuning small language models for domain-specific decision-making is required.

• The role involves designing and building foundational, reusable intelligence layers for long-running business processes, working closely with the backend/orchestration layer rather than just prompt-based applications.

Interested candidates are requested to share your CV to [Confidential Information] or whatsapp @7672046061