{"id":1228029,"url":"https://alion.io/job/algoleap-technologies-engineering-lead","title":"Engineering Lead - Artificial Intelligence/Machine Learning","company":{"id":3800168,"name":"Algoleap","domain":"algoleap.com","url":"https://alion.io/company/algoleap-technologies","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, 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choosing the right approach for each: document and image extraction or classification, predictive models, workflow automation, LLM-based agents and more.\n\n- Apply AI engineering best practices across every project: prompt design, retrieval strategies, evaluation frameworks, regression testing and version control for models and prompts.\n\n- Set and enforce model governance standards: approval workflows, risk and bias assessment, and documentation for every model promoted to production.\n\n- 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.\n\n- 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.\n\n- 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.\n\n- 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.\n\n- Mentor engineers on AI engineering practices, and build a shared standard for how the team designs, tests and ships AI systems.\n\n- Explain what an AI system can and cannot do, in plain terms, to non-technical stakeholders and leadership, so expectations stay realistic.\n\n- Track new AI and machine learning techniques, and test them against real business needs rather than adopting them for their own sake.\n\nWhat You Bring:\n\n- 8+ years of software engineering experience, including 3+ years focused on applied AI or machine learning in production.\n\n- 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.\n\n- Strong programming skills in Python or a comparable language, and fluency with standard AI and ML tooling: model frameworks, orchestration frameworks and vector databases.\n\n- Experience building observability into AI systems: logging, tracing, monitoring and alerting for model behavior in production.\n\n- Experience with model governance: approval workflows, risk and bias assessment, and documentation standards for models moving into production.\n\n- Working knowledge of token economics and compute cost management for AI systems at scale.\n\n- Experience building dashboards or metrics systems that track model and system performance over time.\n\n- A track record of leading or mentoring engineers and setting technical direction, not only contributing as an individual.\n\n- 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.\n\n- Clear communication skills.\n\n- You explain technical trade-offs to engineers and non-technical stakeholders alike, without losing precision.\n\nNice to Have:\n\n- Experience with document or image classification and extraction, as one of several AI domains you have worked in.\n\n- Experience with a dashboarding tool such as Power BI, Tableau or Grafana, used for tracking model and system metrics.\n\n- Familiarity with prompt versioning or evaluation frameworks used for regression testing model outputs.\n\n- Exposure to a data-intensive industry, such as real estate, financial services or health care.\nSkills\nPython, Machine Learning, Artificial Intelligence, Generative AI, Computer Vision, Tableau, Power BI, Grafana, 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