AI/ML Engineer III

Scope: Translate business goals into measurable ML goals (KPIs, acceptance thresholds) in collaboration with PMs and data scientists. Lead the translation of ambiguous product needs into clear ML metrics and success criteria. Own the full lifecycle from prototyping (incl. deep learning and GenAI) to deployment and monitoring. Develop and maintain observability dashboards and alerts tied to ML metrics and feature drift. Run and safeguard models in real time Champion cross-functional collaboration & governance Pilot new ML tools/frameworks, leading integration into production where appropriate. Architect data strategy, championing reproducibility, traceability, and quality across the ML stack Spearhead adoption of emerging ML trends; run strategic POCs and lead production rollouts of state-of-the-art techniques. Act as a cross-org ML thought leader—aligning product, infra, legal, and UX on responsible ML. Key Deliverables by Level Level Title Key Deliverables Level 3 AI/ML Engineer III Scalable ML pipelines with automated training, validation, and deployment workflows Deployed ML solutions integrated with Astreya’s managed service platforms (e.g., NLP for ticket routing) Dashboards for monitoring inference quality and data drift MLOps pipelines with CI/CD practices Essential Duties and Responsibilities (All Levels): Assist in data cleaning, feature engineering, testing basic ML models, write and debug simple scripts Develop ML modules, assist in deployment, support data pipelines, contribute to documentation and unit testing Support data preparation, model training under guidance, debug code, attend knowledge sessions Develop and maintain smaller AI modules (e.g., anomaly detection), assist in deployments, write technical documentation Lead development of scalable ML models, integrate into ITSM systems, ensure compliance and performance metricsArchitect end-to-end AI platforms, oversee cross-domain projects (e.g., NLP for service desk, CV for asset tracking) Lead ML solution design, own production deployments, optimize inference models, drive MLOps practices Architect end-to-end solutions for AI-driven services (e.g., IT ticket routing, network anomaly detection), lead AI projects Education and/or Work Experience Requirements: Minimum Requirements: Bachelor’s degree in Computer Science,Data Science, IT, or a related field.Master’s preferred or equivalent experience for senior levels Level 3: 4–6 years experience in ML/AI implementation and deployment Preferred Certifications (All Levels): Google Cloud Professional Machine Learning Engineer TensorFlow Developer Certificate Knowledge, Skills & Abilities (KSAs): Machine Learning techniques (regression, classification, clustering) Deep Learning architectures (CNNs, RNNs, Transformers, LLMs) NLP (tokenization, BERT, prompt engineering) Big Data fundamentals (Spark, Hadoop) Model interpretability, ethics in AI, bias detection Cloud-native AI services (GCP Vertex AI) Data governance, security, and ethical AI practices Programming: Python, Apps Script, SQL Frameworks: TensorFlow, PyTorch, scikit-learn, HuggingFace Tools: Git, Docker, Kubernetes, Airflow, MLflow,Jupyter, Postman Data pipeline skills: SQL, Pandas, data APIs Deployment: Flask/FastAPI, CI/CD, REST APIs, cloud functions Strong analytical and debugging skills Translate business problems into AI solutions Communicate effectively with technical and non-technical stakeholders Work under Agile or DevOps-based workflows Stay current with research and emerging technologies Rapidly learn new AI concepts and tools Translate business challenges into ML solutions Communicate technical findings to non-technical stakeholders Handle ambiguity and balance research with delivery Collaborate across globally distributed teams Competency Technical Expertise Understands basic ML/DL principles Codes in Python/R Familiarity with AI/ML tools such as Jupyter, scikit-learn, or TensorFlow (basic use) Applies supervised/unsupervised ML methods Proficient in TensorFlow/PyTorch Uses cloud ML services Familiar with ML pipelines Documents technical solutions and contributes to code reviews Designs and builds production-grade models Uses MLflow, Airflow, CI/CD tools Experience with model deployment and monitoring Owns end-to-end AI/ML solutions including architecture, training, deployment, and monitoring Leads development of enterprise-wide AI/ML strategies and platforms Drives model optimization at scale Understands data engineering best practices Defines org-wide AI/ML standards Oversees architecture for reusable platforms Directs ML model governance and compliance Evaluates and mitigates risks related to fairness, privacy, and regulatory requirements Problem Solving & Innovation Solves small coding and data cleaning problems Ability to analyze and clean datasets Identifies root causes in data/model issues Applies ML solutions to scoped problems Effective in debugging and troubleshooting code and data issues Selects and tunes algorithms for real-world impact Innovates within team on novel use cases Anticipates platform-wide AI needs Designs scalable solutions to business-wide problems Champions reusability and standardization across teams Designs AI architectures integrated into critical systems (e.g., service desks, observability) Drives disruptive AI innovation Aligns AI/ML initiatives with enterprise transformation goals Provides strategic oversight for all AI initiatives and cross-org alignment Collaboration & Communication Good communication and team collaboration skills Shares ideas in meetings Communicates findings clearly to peers Contributes to documentation and demos Collaborates cross-functionally to integrate models into services Explains model behavior to technical and semi-technical audiences Coaches junior team members Interprets results and presents actionable insights to stakeholders Builds trust with cross-functional teams and leadership Acts as primary AI contact for programs Engages with external partners/vendors on AI innovation Tracks simple work using task tools Documents code and data usage Delivers discrete ML components Manages tasks independently Leads projects through design, development, testing, and rollout Owns project timeline and quality Familiar with advanced ML topics (e.g., transformers, reinforcement learning, LLM fine-tuning) Coordinates complex programs and integrations Leads cross-functional AI initiatives Drives data quality and governance initiatives for reliable model outcomes Facilitates cross-functional solutioning between product, IT, and operations Oversees multi-team programs Owns delivery of strategic AI initiatives across departments Defines AI success metrics, compliance frameworks, and model governance structures Strategic Thinking & Leadership Understands team mission Adopts best practices Takes direction and accepts feedback constructively Builds and evaluates supervised/unsupervised models independently Provides input on technical direction Mentors junior engineers Designs scalable models and pipelines for production use Defines best practices and technical vision Influences product and engineering roadmap Balances model performance with business objectives and ethical guidelines Sets the AI/ML vision and roadmap aligned with business growth goals Establishes AI strategy, ethics, and governance Influences external clients and industry engagement Physical Requirements: Travel occasionally required for team collaboration, client meetings, or workshops Flexibility to work across global time zones when needed