Engineering Lead AI ML Gen AI

algoleap; Hyderabad; On-site; INR 3,800,000 - 6,000,000; Full time

Job summary

AlgoLeap Technologies Pvt Ltd. seeks an senior AI/ML engineer to design and deploy AI solutions across business problems, including extraction, classification and LLM-based agents. You will apply best practices, governance, and observability while mentoring engineers and communicating trade-offs to stakeholders.

The role emphasizes cost-aware model deployment, dashboard-based monitoring, and selecting the right AI approach per task to deliver tangible business outcomes.

Qualifications

• 8+ years of software engineering experience with 3+ years in applied AI/ML production
• Hands-on with AI solutions including extraction, classification, and LLM-based agents
• Strong Python skills and familiarity with AI/ML tooling
• Experience building observability for AI systems and model governance standards
• Knowledge of token economics and cost management for AI systems at scale
• Ability to lead/mentor engineers and set technical direction
• Strong business acumen to translate AI to concrete outcomes
• Clear communication to engineers and non-technical stakeholders

Responsibilities

• Design and build AI solutions across business problems like extraction, classification, or predictive models
• Apply engineering best practices: prompts, retrieval, evaluation, and version control
• Set governance standards: approvals, bias risk assessment, and documentation
• Build observability: logging, tracing, monitoring for latency and failures
• Own token economics and compute costs for LLMs and AI systems
• Develop dashboards tracking accuracy, latency, cost, throughput and drift
• Select AI approaches per problem (LLM, classical ML, CV, or mix)
• Mentor engineers and establish team design/test/shipping standards
• Explain AI capabilities and limits to stakeholders to manage expectations
• Track new AI/ML techniques and test against business needs

Skills

AI engineering

Python

Observability

Model governance

Cost management

Dashboarding

Mentoring

Business acumen

Tools

ML 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.

Requirements: