AI/ML Engineer II

🔥 2 hours ago

🇮🇳 India – Remote

⏰ Full Time

🟢 Junior

🤖 Machine Learning Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 13%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Astreya

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2001

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Astreya is a leading global provider of IT Managed Services and Technology Solutions, known for its innovative approach to digital engineering and IT logistics. The company focuses on empowering businesses to excel in today's dynamic digital landscape by maximizing productivity and fostering innovation. Astreya offers a range of services including Data Center & Network Management, Digital Workplace Services, Next-Gen Digital Engineering, and Cybersecurity Services. With a commitment to excellence and a focus on operational frameworks, Astreya aims to transform technology into a valuable strategic asset for organizations worldwide.

📋 Description

• Translate business goals into measurable ML goals, KPIs, and acceptance thresholds with PMs and data scientists • Translate ambiguous product needs into clear ML metrics and success criteria • Own the full lifecycle from prototyping, including deep learning and GenAI, through 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 and governance • Pilot new ML tools and frameworks and lead production integration where appropriate • Architect data strategy focused on reproducibility, traceability, and quality across the ML stack • Lead adoption of emerging ML trends through strategic POCs and production rollouts • Align product, infrastructure, legal, and UX stakeholders on responsible ML • Assist with data cleaning, feature engineering, basic ML model testing, scripting, deployment, data pipelines, documentation, and unit testing as applicable by level • Lead ML solution design, production deployments, inference optimization, MLOps practices, and AI projects at higher levels • Architect end-to-end AI services and platforms, including IT ticket routing, network anomaly detection, service desk NLP, and asset-tracking computer vision • Define enterprise AI roadmaps, governance, compliance, model standards, and strategic AI initiatives at senior levels • Integrate scalable ML models into ITSM systems and oversee cross-domain projects • Analyze and clean datasets, identify data/model root causes, select and tune algorithms, and design scalable solutions • Collaborate cross-functionally, explain model behavior, mentor junior team members, present insights, and engage partners or clients • Manage projects through design, development, testing, rollout, timelines, quality, and multi-team delivery • Establish AI strategy, ethics, governance, technical vision, and organizational standards

🎯 Requirements

• Level 1: 1–2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch • Level 2: 1–2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch • Level 3: 4–6 years experience in ML/AI implementation and deployment • Level 4: 7–9 years experience; domain expertise (e.g., IT operations or security AI) • Level 5: 10+ years in software engineering with AI/ML leadership experience • Preferred certifications include Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, TensorFlow Developer Certificate, Databricks Certified Machine Learning Professional, and Kubernetes or Docker certification for MLOps roles • Machine learning techniques including regression, classification, and clustering • Deep learning architectures including CNNs, RNNs, Transformers, and LLMs • NLP including tokenization, BERT, and prompt engineering • Big Data fundamentals including Spark and Hadoop • Model interpretability, AI ethics, and bias detection • Cloud-native AI services including AWS SageMaker, GCP Vertex AI, and Azure ML • Data governance, security, and ethical AI practices • Python required; Java/C++ optional; SQL • Frameworks including TensorFlow, PyTorch, scikit-learn, and Hugging Face • Tools including Git, Docker, Kubernetes, Airflow, MLflow, Jupyter, and Postman • Data pipeline skills including SQL, Pandas, and data APIs • Deployment technologies including Flask/FastAPI, CI/CD, REST APIs, and cloud functions • Strong analytical and debugging skills • Ability to translate business problems into AI solutions • Effective communication with technical and non-technical stakeholders • Experience working under Agile or DevOps-based workflows • Ability to collaborate across globally distributed teams