Engineering Lead AI ML Gen AI

algoleap; Hyderabad; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

algoleap seeks an experienced AI/ML Engineer to design and deploy solutions across document and image extraction, forecasting, and intelligent agents. You will implement governance, observability, and cost strategies while mentoring engineers and translating technical decisions for leadership.

You will work with Python and standard AI tooling, building production-grade pipelines, dashboards, and scalable models to drive tangible business outcomes.

Qualifications

• 8+ years of software engineering experience with 3+ years in applied AI/ML in production.
• Hands-on experience building and deploying AI solutions across document/image extraction, classification, models, or LLM-based agents.
• Strong Python skills and fluency with standard AI/ML tooling and data platforms.

Responsibilities

• Design and build AI solutions for diverse business problems, selecting the best approach for each task.
• Apply AI engineering best practices: prompts, retrieval, evaluation, regression testing, and version control.
• Establish model governance: approvals, risk/bias assessment, and production documentation.
• Instrument systems with observability: logging, tracing, and monitoring of inputs/outputs and latency.
• Own cost and compute management for LLM-based and other AI systems; optimize for value.
• Develop dashboards to track metrics like accuracy, latency, cost, and drift across production AI.
• Evaluate AI approaches (LLM, classical ML, CV, or hybrids) based on business need.
• Mentor engineers and set technical direction; communicate trade-offs clearly to stakeholders.

Skills

Leadership

Communication

Business acumen

Mentoring

Stakeholder management

Tools

Python

Model frameworks

Orchestration frameworks

Vector databases

Job description

Job Description:

• Design and build AI solutions across a range of business problems, choosing the right approach for each: document and image extraction or classification, predictive models, workflow automation, LLM-based agents and more.


• Apply AI engineering best practices across every project: prompt design, retrieval strategies, evaluation frameworks, regression testing and version control for models and prompts.


• Set and enforce model governance standards: approval workflows, risk and bias assessment, and documentation for every model promoted to production.


• Build observability into every AI system: logging, tracing and monitoring for inputs, outputs, latency and failure modes, so issues surface before they reach the business.


• Own token economics and compute cost across LLM-based and other AI systems: track cost per request, choose the right model size for each task, and balance accuracy against spend.


• Build and maintain a dashboard that tracks the metrics that matter, such as accuracy, latency, cost, throughput, drift and error rate, across every AI system in production.


• Evaluate and select the right AI approach for each problem, whether that is an LLM, a classical machine learning model, computer vision or a mix, based on what the problem actually needs rather than what is fashionable.


• Mentor engineers on AI engineering practices, and build a shared standard for how the team designs, tests and ships AI systems.


• Explain what an AI system can and cannot do, in plain terms, to non-technical stakeholders and leadership, so expectations stay realistic.


• Track new AI and machine learning techniques, and test them against real business needs rather than adopting them for their own sake.

What You Bring

• 8+ years of software engineering experience, including 3+ years focused on applied AI or machine learning in production.


• Hands‑on experience building and deploying a range of AI solutions, such as document or image extraction and classification, predictive models, recommendation systems, or LLM-based agents and assistants.


• Strong programming skills in Python or a comparable language, and fluency with standard AI and ML tooling: model frameworks, orchestration frameworks and vector databases.


• Experience building observability into AI systems: logging, tracing, monitoring and alerting for model behavior in production.


• Experience with model governance: approval workflows, risk and bias assessment, and documentation standards for models moving into production.


• Working knowledge of token economics and compute cost management for AI systems at scale.


• Experience building dashboards or metrics systems that track model and system performance over time.


• A track record of leading or mentoring engineers and setting technical direction, not only contributing as an individual.


• Strong business acumen: you translate an AI capability into a concrete business outcome, and you know when a simpler, non-AI solution is the right call.


• Clear communication skills. You explain technical trade-offs to engineers and non-technical stakeholders alike, without losing precision.

Nice to Have

• Experience with document or image classification and extraction, as one of several AI domains you have worked in.


• Experience with a dashboarding tool such as Power BI, Tableau or Grafana, used for tracking model and system metrics.


• Familiarity with prompt versioning or evaluation frameworks used for regression testing model outputs.


• Exposure to a data-intensive industry, such as real estate, financial services or health care.