AI Engineering Lead

Blend360; Hyderabad; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Blend360 in Hyderabad, India, seeks an experienced AI Engineering Lead to drive end-to-end AI product delivery and scalable ML systems in production.

You will mentor engineers, design RAG and LLM-based solutions, implement MLOps pipelines, and collaborate with stakeholders to translate complex AI challenges into impactful business outcomes.

Excellent communication, Python expertise, cloud proficiency, and a track record of delivering AI services are essential.

Qualifications

• 6+ years of hands-on experience building, deploying, and maintaining AI solutions in production environments.
• Expert-level proficiency in Python with strong software engineering practices (Git, code review, testing).
• Proven expertise in designing and implementing RAG systems, chunking strategies, embeddings, retrieval optimization, and evaluation methodologies.
• Solid experience with cloud platforms (AWS, Azure, or GCP) including containerization, orchestration, and infrastructure management.
• Demonstrated track record with MLOps/LLMOps tools and frameworks (MLflow, Weights & Biases, or equivalent).
• Strong hands-on experience with LLM versioning, model management, and experiment tracking.
• Experience designing evaluation frameworks, datasets, and structured experimentation.
• Experience leading technical teams and mentoring engineers.
• Excellent communication skills for collaboration with engineers, stakeholders, and leadership.

Responsibilities

• Lead end-to-end AI project delivery with governance frameworks and transparent risk communication.
• Design and architect robust AI systems including RAG and LLM-powered solutions for production environments.
• Assess technical approaches (prompting, RAG, fine-tuning, classical ML, hybrid) grounded in business needs.
• Develop advanced prompt engineering techniques, including instruction design and few-shot learning.
• Implement evaluation frameworks with custom metrics and structured go/no-go gates.
• Execute data-driven experiments across prompts, retrievers, chunking strategies, and models.
• Mentor junior engineers and cultivate a collaborative problem-solving culture.
• Contribute to business development through proposals and feasibility studies.

Skills

Years of experience

Python

LLM/RAG

Cloud platforms

MLOps/LLMOps

Team leadership

Stakeholder communication

Tools

MLflow

Weights & Biases

Databricks

Terraform

Job description

We're looking for an experienced and visionary AI Engineering Lead to join our growing team in Hyderabad, India. In this role, you'll lead the design, development, and deployment of cutting‑edge AI solutions that drive real business impact. You'll combine technical excellence with strategic thinking, mentoring talented engineers while collaborating closely with stakeholders to translate complex AI challenges into scalable, production‑ready systems. This is an opportunity to shape the future of AI engineering within our organization while fostering a culture of innovation, transparency, and continuous learning.

Lead end‑to‑end AI project delivery with clear governance frameworks, ensuring transparent communication of risks, tradeoffs, and technical decisions to clients and internal stakeholders

Design and architect robust AI systems, including RAG (Retrieval‑Augmented Generation) systems, agentic frameworks, and LLM‑powered solutions optimized for production environments

Conduct feasibility assessments to determine the optimal technical approach-whether prompting, RAG, fine‑tuning, classical machine learning, or hybrid solutions-grounded in evidence and business requirements

Develop and implement advanced prompt engineering techniques, including instruction design, few‑shot learning, structured outputs, and tool/agent orchestration

Design comprehensive evaluation frameworks incorporating LLM‑as‑a‑judge methodologies, custom metrics (recall@k, precision@k), and structured go/no‑go decision gates

Execute rigorous, data‑driven experiments across prompts, retrievers, chunking strategies, and models, documenting findings and iterating based on evidence rather than intuition

Identify, categorize, and mitigate model failure modes including hallucinations, retrieval gaps, and instruction‑following errors

Build and maintain scalable inference infrastructure, CI/CD pipelines, and deployment automation for AI and machine learning models

Design and implement MLOps/LLMOps automation across the full lifecycle: experiment tracking, model versioning, deployment, monitoring, retraining, and observability

Architect APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and security

Mentor junior engineers, fostering technical growth and a collaborative problem‑solving culture

Contribute to business development initiatives, including proposal writing and feasibility studies for new AI opportunities

Define ethical boundaries and guardrails for AI systems, ensuring responsible and transparent deployment

Required Skills and Experience:

6+ years of hands‑on experience building, deploying, and maintaining AI solutions in production environments

Expert‑level proficiency in Python with strong software engineering practices (Git, code review, testing)

Proven expertise in designing and implementing RAG systems, including chunking strategies, embedding models, retrieval optimization, reranking, and evaluation methodologies

Solid experience with cloud platforms (AWS, Azure, or GCP) including containerization, orchestration, and infrastructure management

Demonstrated track record with MLOps/LLMOps tools and frameworks (MLflow, Weights & Biases, or equivalent)

Strong hands‑on experience with LLM versioning, model management, and experiment tracking

Practical expertise in designing evaluation frameworks, custom metrics, dataset curation, and structured experimentation

Experience designing and implementing event‑driven architectures, RESTful APIs, and microservices

Proven ability to lead technical teams, mentor engineers, and drive collaborative problem‑solving

Excellent communication skills-equally comfortable engaging engineering teams, technical stakeholders, and senior leadership

Strong analytical and decision‑making abilities with a detail‑oriented approach to complex technical challenges

Experience defining and communicating AI system limitations, risks, and ethical considerations to diverse audiences

Preferred Qualifications:

Experience with Databricks MLOps platform or similar enterprise ML platforms

Hands‑on experience with LLM fine‑tuning and transfer learning techniques

Proven expertise building agentic GenAI systems and multi‑step reasoning frameworks

Knowledge of Infrastructure as Code (Terraform, CloudFormation, or equivalent)

Experience implementing security, compliance, and observability solutions for AI services

Strong background in classical machine learning and statistical methods

Active contributions to open‑source AI/ML projects

Experience with advanced prompt engineering frameworks and tool‑use optimization

Background in hiring, team building, and organizational development

Understanding of AI ethics, bias mitigation, and responsible AI practices

Language Requirements:

Advanced English proficiency (required for effective communication with global teams and stakeholders)