Engineering Lead ( AI/ML, Gen AI)

Algoleap Technologies; Hyderabad; On-site; INR 400,000 - 700,000; Full time

Job summary

Algoleap Technologies Pvt Ltd is seeking an experienced AI Engineer to design and deploy AI solutions across business problems. You will lead AI engineering practices, establish governance, and mentor the team to ship reliable models in production.

The role emphasizes prompt design, observability, cost control, and strong communication with stakeholders to align technical decisions with business outcomes.

Qualifications

• 8+ years in software engineering with 3+ years in applied AI/ML production.
• Hands-on experience deploying AI solutions including extraction and classification.
• Strong Python and AI tooling fluency; experience with governance and observability.

Responsibilities

• Architect and ship AI solutions across document/image extraction, classification and ML models.
• Mentor engineers and set technical direction for AI systems.
• Establish governance, testing, and cost-management practices for production AI.
• Communicate trade-offs clearly to technical and non-technical stakeholders.

Skills

AI engineering

Prompt design

Model governance

Observability

Cost management

Leadership

Communication

Tools

Python

Vector databases

Model frameworks

Orchestration frameworks

Dashboarding tools

Job description

Algoleap Technologies Pvt Ltd | Full time

• Design and build AI solutionsacross a range of business problems, choosing the right approach for each:document and image extraction or classification, predictive models, workflowautomation, LLM-based agents and more.
• Apply AI engineering bestpractices across every project: prompt design, retrieval strategies, evaluationframeworks, regression testing and version control for models and prompts.
• Set and enforce modelgovernance standards: approval workflows, risk and bias assessment, anddocumentation for every model promoted to production.
• Build observability into everyAI system: logging, tracing and monitoring for inputs, outputs, latency andfailure modes, so issues surface before they reach the business.
• Own token economics and computecost across LLM-based and other AI systems: track cost per request, choose theright model size for each task, and balance accuracy against spend.
• Build and maintain a dashboardthat 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 rightAI approach for each problem, whether that is an LLM, a classical machinelearning model, computer vision or a mix, based on what the problem actuallyneeds rather than what is fashionable.
• Mentor engineers on AIengineering practices, and build a shared standard for how the team designs,tests and ships AI systems.
• Explain what an AI system canand cannot do, in plain terms, to non-technical stakeholders and leadership, soexpectations stay realistic.
• Track new AI and machinelearning techniques, and test them against real business needs rather thanadopting them for their own sake.

What You Bring

• 8+ years of softwareengineering experience, including 3+ years focused on applied AI or machinelearning in production.
• Hands-on experience buildingand deploying a range of AI solutions, such as document or image extraction andclassification, predictive models, recommendation systems, or LLM-based agentsand assistants.
• Strong programming skills inPython or a comparable language, and fluency with standard AI and ML tooling:model frameworks, orchestration frameworks and vector databases.
• Experience buildingobservability into AI systems: logging, tracing, monitoring and alerting formodel behavior in production.
• Experience with modelgovernance: approval workflows, risk and bias assessment, and documentationstandards for models moving into production.
• Working knowledge of tokeneconomics and compute cost management for AI systems at scale.
• Experience building dashboardsor metrics systems that track model and system performance over time.
• A track record of leading ormentoring engineers and setting technical direction, not only contributing asan individual.
• Strong business acumen: youtranslate an AI capability into a concrete business outcome, and you know whena simpler, non-AI solution is the right call.
• Clear communication skills. Youexplain technical trade-offs to engineers and non-technical stakeholders alike,without losing precision.

Nice to Have

• Experience with document orimage classification and extraction, as one of several AI domains you haveworked in.
• Experience with a dashboardingtool such as Power BI, Tableau or Grafana, used for tracking model and systemmetrics.
• Familiarity with promptversioning or evaluation frameworks used for regression testing model outputs.
• Exposure to a data-intensiveindustry, such as real estate, financial services or health care.