Senior Agentic AI Engineer

BSC Solutions India Pvt Ltd; Bengaluru; On-site; INR 4,200,000 - 8,500,000; Full time

Job summary

BSC Solutions India Pvt Ltd in Bengaluru, on-site, is seeking a Senior AI Engineer to lead production-grade AI systems. You will own architecture, deployment, and optimization of GenAI applications, including autonomous agents, RAG pipelines, and multi-agent workflows.

The role emphasizes scalable cloud deployments on AWS, robust observability, and mentoring junior engineers, with close collaboration with product, data, and DevOps teams.

Qualifications

• Bachelor's degree in CS or related field
• 7+ years of professional software engineering and ML in production
• Experience building Agentic AI systems with tool integration
• Expertise in Prompt Engineering and LLM application development
• Production experience with RAG architectures and vector databases
• Proficiency in Python and ML libraries (NumPy, Pandas, Scikit-learn)
• Hands-on experience with LangChain, LlamaIndex, or native API orchestration
• Experience deploying AI/ML on AWS and maintaining CI/CD pipelines

Responsibilities

• Architect & Deploy GenAI solutions end-to-end
• Engineer production-grade agentic workflows
• Build, evaluate, and scale RAG systems
• Develop conversational AI interfaces
• Advance prompt engineering techniques for determinism
• Scale AI workloads on AWS with monitoring
• Design and maintain CI/CD pipelines (GitHub Actions, Jenkins)
• Improve observability and reliability of production systems
• Mentor junior engineers and lead architectural reviews
• Engage with clients as a technical lead

Skills

Agentic AI

Prompt Engineering

Python

LLM Orchestration

AWS Cloud

CI/CD Automation

Education

Bachelor's degree in CS or related field

Tools

LangChain

LlamaIndex

GitHub Actions

Jenkins

LangSmith

TruLens

OpenTelemetry

SageMaker

Bedrock

Pinecone

Milvus

Qdrant

Chroma

pgvector

Job description

Senior AI Engineer (Agentic Systems)

Location: Bengaluru, India | On-Site

Role Level: Senior Engineer (L5 / Equivalent)

About the Job

As a Senior AI Engineer, you will stand at the intersection of production systems engineering and state-of-the-\xE2\x80\x99art Generative AI. We are building next-generation intelligent systems that go far beyond basic text generationtransforming foundational models into autonomous, tool-\xE2\x80\x99using multi-agent workflows and high-performance context-aware engines.

In this role, you will own the architectural vision, deployment, and optimization of enterprise-grade AI applications. You will design robust Retrieval-Augmented Generation (RAG) pipelines, construct autonomous agent networks, build conversational systems, and harden these solutions for scale, observability, and resilience on cloud infrastructure. Working closely with product managers, data teams, DevOps, and external client stakeholders, you will translate ambiguous business requirements into high-throughput, production-ready AI software while mentoring junior engineers across the organization.

Responsibilities

• Architect & Deploy GenAI Solutions: Design, build, benchmark, and deploy end-to-end machine learning and Generative AI systems tailored to real-world enterprise use cases.
• Agentic AI & Orchestration: Engineer production-grade agentic workflows, including autonomous agents, function calling/tool-use pipelines, reflection loops, and collaborative multi-agent architectures.
• RAG Architecture: Build, evaluate, and scale Retrieval-Augmented Generation systems using semantic search, modern embedding models, and vector database retrieval strategies (chunking, hybrid search, reranking).
• Conversational Systems: Build and optimize high-throughput chatbots and conversational AI interfaces using modern LLM orchestration frameworks (LangChain, LlamaIndex, or internal engines).
• Advanced Prompt Engineering: Formulate, evaluate, and iterate systematic prompt engineering techniques (chain-of-thought, few-shot conditioning, structured outputs) to maximize output determinism and minimize hallucination.
• Cloud & Infrastructure Scaling: Deploy, scale, and monitor distributed AI workloads leveraging AWS cloud services (ECS/EKS, Lambda, Sagemaker/Bedrock, CloudWatch) with auto-scaling capabilities.
• CI/CD & MLOps: Design, automate, and maintain continuous integration and deployment pipelines (GitHub Actions, Jenkins) for seamless testing, model rollouts, and regression tracking.
• Production Reliability & Observability: Establish evaluation frameworks (evals), latency optimization, cost management, and observability tracing (e.g., OpenTelemetry, LangSmith, TruLens) for production reliability.
• Technical Leadership & Client Engagement: Serve as the technical bridge between client stakeholders and engineering, translating high-level business goals into technical architectures, and actively mentor junior engineers in code quality and system design.
• Proven track record acting as a client-facing technical lead or enterprise solutions architect.
• Experience defining engineering standards, running architectural design reviews, and mentoring mid-level and junior engineers.

Minimum Qualifications

• Bachelors degree in Computer Science, Software Engineering, Mathematics, Data Science, or equivalent practical experience.
• 7+ years of professional software engineering and machine learning experience in production environments.
• Demonstrated production experience building Agentic AI systems, including tool integration, autonomous reasoning, and multi-agent coordination.
• Expertise in Prompt Engineering & LLM Application Development, including structured outputs, prompt optimization, guardrails, and deterministic evaluation frameworks.
• Production experience implementing RAG architectures, vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma, pgvector), chunking optimizations, and semantic retrieval.
• Advanced programming proficiency in Python, with strong command of core libraries (NumPy, Pandas, Scikit-learn) and deep learning / GenAI ecosystems.
• Hands-on experience developing conversational AI pipelines using frameworks such as LangChain, LlamaIndex, or native API orchestration.
• Practical experience deploying and managing AI/ML workloads on AWS cloud infrastructure.
• Hands-on experience building and maintaining CI/CD pipelines using GitHub Actions or Jenkins.