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