AI Engineer

Minfy; Hyderabad; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Minfy is hiring an AI Engineer to contribute across customer engagements, setting technical direction and staying hands-on to deliver business outcomes. You will build reusable assets, patterns, and standards to scale AI engineering and mentor peers across engagements.

You will lead architecture decisions, design robust pipelines, and run systematic experiments to demonstrate GenAI gains over conventional ML, while ensuring timely delivery and clear technical documentation.

Qualifications

• 10+ years in applied machine learning / data science with hands-on experience shipping production ML systems.
• Hands-on experience with LLMs in production: prompt engineering, retrieval-augmented generation, embeddings, and evaluation.
• Hands-on experience building agentic systems including tool use, multi-step workflows, orchestration, and guardrails.
• Experience with Amazon Bedrock or comparable managed LLM platforms.
• Strong communication skills to explain modeling decisions, trade-offs, and results to engineers, data scientists, and senior stakeholders.
• Customer-facing or stakeholder-facing experience: building trust and managing competing priorities.
• Experience switching domains across engagements without losing technical rigor.
• Proven leadership through mentoring engineers, driving design decisions, and setting standards.
• Strong track record taking models from experimentation to production, including offline-to-online validation.

Responsibilities

• Own the technical strategy on customer engagements, making architecture and modeling decisions and being accountable for results.
• Ramp quickly into unfamiliar domains, scoping the right approach for each data context, constraints, and timeline.
• Stay hands-on: build pipelines, train and evaluate models, run experiments, and write critical code.
• Set the technical bar and mentor other engineers through design reviews and pairing.
• Act as a senior technical contact with customers, communicating progress, risks, and results to engineers and senior stakeholders.
• Engineer features and datasets from large-scale customer data, and integrate signals into model training.
• Design, build, and evaluate LLM and agentic solutions including prompts, retrieval, and orchestration.
• Design and run structured experiments to measure GenAI gains over traditional ML methods.
• Own model and agent development end-to-end, including feature integration and hyperparameter optimization.
• Define and run evaluation frameworks with metrics, evaluation harnesses, and ablations.
• Establish path to production: serving, latency, cost, shadow testing, and AB readiness.

Skills

Production ML systems

LLMs in production

Agentic systems

Mentoring engineers

Design decisions

Stakeholder communication

Python

AWS

MLOps

LLMOps

PyTorch

TensorFlow

Education

Advanced degree in Computer Science / Machine Learning / Statistics

Tools

Amazon Bedrock

PyTorch

TensorFlow

Spark

SQL

Job description

Job Description

We are hiring an AI Engineer to serve as a technical contributor across multiple customer engagements. On each engagement you will set the technical direction, make the key modeling decisions, and stay hands‑on throughout, acting as a senior technical point of contact with the customer, explaining trade‑offs, managing expectations, and turning results into clear business recommendations and outcomes. Across engagements you will help build the reusable assets, patterns, and technical standards that raise the bar for a scaling AI engineering organization.

What You’ll Do

• Own the technical strategy on customer engagements, making the architecture and modeling decisions and being accountable for the results.
• Ramp quickly into unfamiliar domains and problem types, scoping the right approach for each customer's data, constraints, and timeline.
• Stay hands‑on: build the pipelines, train and evaluate the models, run the experiments, and write the critical code.
• Set the technical bar and support other engineers through design reviews, mentorship, and pairing.
• Act as a senior technical point of contact with customers, communicating progress, risks, and results to both engineers and senior stakeholders, and managing expectations through ambiguity.
• Engineer features and datasets from large‑scale customer data, and integrate signals into ML model training and runs.
• Design, build, and evaluate LLM and agentic solutions including prompt and context design, retrieval, tool use, multi‑step agent workflows, and orchestration.
• Design and run structured, parallel experiments that measure the gains from GenAI approaches over strong conventional ML methods.
• Own model and agent development end to end, including feature integration, hyperparameter optimization, and error analysis.
• Define and run the evaluation framework including task‑level quality metrics, LLM and agent evaluation harnesses, and ablation studies.
• Establish the path to production: model and agent serving, latency and cost management, shadow‑mode testing, A/B framework readiness, and guardrail metrics.
• Build reusable accelerators, reference architectures, and internal standards that carry from one engagement to the next.
• Deliver clear technical documentation and lead knowledge‑transfer sessions so each customer's teams can operate and iterate independently after handoff.

Required Qualifications

• 10+ years in applied machine learning / data science, with deep hands‑on experience in building and shipping production ML systems across multiple problem domains.
• Hands‑on experience with LLMs in production: prompt and context engineering, retrieval‑augmented generation, embeddings, and reasoning about evaluation, latency, and cost.
• Hands‑on experience building agentic systems including tool use and function calling, multi‑step workflows, orchestration frameworks, and agent evaluation and guardrails.
• Experience with Amazon Bedrock or comparable managed LLM platforms.
• Strong communication skills, able to explain modeling decisions, trade‑offs, and results clearly to engineers, data scientists, and senior business stakeholders, and to manage expectations through ambiguity.
• Customer‑facing or stakeholder‑facing experience: building trust, navigating competing priorities, and serving as a senior technical voice in high‑stakes conversations.
• Comfort working across several engagements and customer contexts at once, switching domains without losing technical rigor.
• A track record of technical leadership through mentoring engineers, driving design decisions, and setting standards.
• Strong track record taking models from experimentation to production, owning the offline‑to‑online validation story (evaluation metrics, ablations, shadow testing, A/B readiness).
• Deep, hands‑on expertise in deep learning and embedding‑based architectures with a major framework (PyTorch or TensorFlow).
• Strong feature engineering on large datasets using the modern data stack (Spark, SQL, distributed data lakes).
• Rigorous experimental methodology including hyperparameter optimization and a disciplined, hypothesis‑driven approach to measuring true lift.
• Hands‑on AWS experience across the ML lifecycle, and strong proficiency in Python.
• Experience in MLOps and LLMOps including model and prompt versioning, monitoring, evaluation pipelines, and reproducible training.

Preferred Qualifications

• Prior experience in a client‑facing consulting or professional‑services delivery environment.
• Advanced degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.