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 timeChampion cross-functional collaboration governancePilot new ML tools/frameworks, leading integration into production where appropriate. Architect data strategy, championing reproducibility, traceability, and quality across the ML stackSpearhead 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 LevelLevelTitleKey DeliverablesLevel 3AI/ML Engineer IIIScalable ML pipelines with automated training, validation, and deployment workflowsDeployed ML solutions integrated with Astreya’s managed service platforms (e. g. , NLP for ticket routing)Dashboards for monitoring inference quality and data driftMLOps pipelines with CI/CD practicesEssential Duties and Responsibilities (All Levels):Assist in data cleaning, feature engineering, testing basic ML models, write and debug simple scriptsDevelop ML modules, assist in deployment, support data pipelines, contribute to documentation and unit testingSupport data preparation, model training under guidance, debug code, attend knowledge sessionsDevelop and maintain smaller AI modules (e. g. , anomaly detection), assist in deployments, write technical documentationLead 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 practicesArchitect end-to-end solutions for AI-driven services (e. g. , IT ticket routing, network anomaly detection), lead AI projectsEducation 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 levelsLevel 3: 4–6 years experience in ML/AI implementation and deploymentPreferred Certifications (All Levels):Google Cloud Professional Machine Learning EngineerTensorFlow Developer CertificateKnowledge, 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 practicesProgramming: Python, Apps Script, SQLFrameworks: TensorFlow, PyTorch, scikit-learn, HuggingFaceTools: Git, Docker, Kubernetes, Airflow, MLflow,Jupyter, PostmanData pipeline skills: SQL, Pandas, data APIsDeployment: Flask/FastAPI, CI/CD, REST APIs, cloud functionsStrong analytical and debugging skillsTranslate business problems into AI solutionsCommunicate effectively with technical and non-technical stakeholdersWork under Agile or DevOps-based workflowsStay current with research and emerging technologiesRapidly learn new AI concepts and toolsTranslate business challenges into ML solutionsCommunicate technical findings to non-technical stakeholdersHandle ambiguity and balance research with deliveryCollaborate across globally distributed teamsCompetency

Technical

ExpertiseUnderstands basic ML/DL principlesCodes in Python/RFamiliarity with AI/ML tools such as Jupyter, scikit-learn, or TensorFlow (basic use)Applies supervised/unsupervised ML methodsProficient in TensorFlow/PyTorchUses cloud ML servicesFamiliar with ML pipelines Documents technical solutions and contributes to code reviewsDesigns 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 monitoringLeads development of enterprise-wide AI/ML strategies and platformsDrives model optimization at scale Understands data engineering best practicesDefines org-wide AI/ML standardsOversees architecture for reusable platformsDirects ML model governance and complianceEvaluates and mitigates risks related to fairness, privacy, and regulatory

requirements

Problem Solving InnovationSolves small coding and data cleaning problemsAbility to analyze and clean datasetsIdentifies root causes in data/model issuesApplies ML solutions to scoped problemsEffective in debugging and troubleshooting code and data issuesSelects and tunes algorithms for real-world impactInnovates within team on novel use casesAnticipates platform-wide AI needsDesigns scalable solutions to business-wide problemsChampions reusability and standardization across teamsDesigns AI architectures integrated into critical systems (e. g. , service desks, observability)Drives disruptive AI innovationAligns AI/ML initiatives with enterprise transformation goalsProvides strategic oversight for all AI initiatives and cross-org alignmentCollaboration CommunicationGood communication and team collaboration skillsShares ideas in meetingsCommunicates findings clearly to peersContributes to documentation and demosCollaborates cross-functionally to integrate models into servicesExplains model behavior to technical and semi-technical audiencesCoaches junior team members Interprets results and presents actionable insights to stakeholdersBuilds trust with cross-functional teams and leadership Acts as primary AI contact for programsEngages with external partners/vendors on AI innovationTracks simple work using task toolsDocuments code and data usageDelivers discrete ML componentsManages tasks independentlyLeads projects through design, development, testing, and rolloutOwns project timeline and qualityFamiliar with advanced ML topics (e. g. , transformers, reinforcement learning, LLM fine-tuning)Coordinates complex programs and integrationsLeads cross-functional AI initiativesDrives data quality and governance initiatives for reliable model outcomesFacilitates cross-functional solutioning between product, IT, and operationsOversees multi-team programsOwns delivery of strategic AI initiatives across departmentsDefines AI success metrics, compliance frameworks, and model governance structuresStrategic Thinking LeadershipUnderstands team missionAdopts best practicesTakes direction and accepts feedback constructivelyBuilds and evaluates supervised/unsupervised models independentlyProvides input on technical direction Mentors junior engineers Designs scalable models and pipelines for production useDefines best practices and technical vision Influences product and engineering roadmapBalances model performance with business objectives and ethical guidelinesSets the AI/ML vision and roadmap aligned with business growth goalsEstablishes AI strategy, ethics, and governanceInfluences external clients and industry engagementPhysical

Requirements

: Travel occasionally required for team collaboration, client meetings, or workshopsFlexibility to work across global time zones when needed.