Senior AIML Engineer
Actively Reviewing the ApplicationsA.P. Moller - Maersk
India, Karnataka, Bengaluru
Full-Time
On-site
Posted 15 hours ago
•
Apply by June 8, 2026
Job Description
We are seeking a seasoned AI/ML Engineer with deep expertise in both traditional machine learning and modern Generative AI/LLM technologies. This role will be instrumental in driving our AI transformation, building enterprise-grade solutions that combine classical ML approaches with cutting-edge GenAI capabilities.
Core AI/ML Development
Essential Skills
Programming & Data Engineering:
Required
We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [email protected].
Core AI/ML Development
- Partner with business, product, and engineering teams to define problem statements, evaluate feasibility, and design AI/ML-driven solutions that deliver measurable business value
- Lead and execute end-to-end AI/ML projects — from data exploration and model development to validation, deployment, and monitoring in production
- Independently design and implement scalable machine learning solutions and data systems, ensuring end-to-end workflows, large-scale analytics, and reliability
- Design and implement RAG (Retrieval Augmented Generation) systems for enterprise knowledge management
- Develop guardrails and safety measures for GenAI applications in production
- Implement cost optimization strategies for LLM inference at scale
- Create synthetic data generation pipelines for model training and testing
- Build and optimize prompt engineering strategies and fine-tuning pipelines
- Drive solution architecture using techniques in data engineering, programming, machine learning, NLP, and computer vision
- Implement and refine feature engineering, monitoring, ML pipelines, deploy models in production
- Build real-time inference APIs with sub-second latency requirements
- Develop forecasting models for demand prediction and supply chain optimization
- Create recommendation systems for route optimization and customer solutions
- Champion the scalability, reproducibility, and sustainability of AI solutions by establishing best practices in model development, CI/CD, and performance tracking
- Ensure readiness for production releases, focusing on testing, monitoring, observability, and maintaining scalability
- Implement comprehensive model versioning, registry, and rollback strategies
- Build automated retraining pipelines and drift detection systems
- Guide junior and associate AI/ML engineers through technical mentoring, code reviews, and solution reviews
- Translate technical outputs into actionable insights for business stakeholders through storytelling and data visualizations
- Drive cross-team and cross-discipline initiatives to optimize workflows and enhance collaboration
- Identify and evangelize the adoption of emerging tools, technologies, and methodologies across teams
Essential Skills
Programming & Data Engineering:
- Advanced proficiency in Python, SQL, PySpark
- Experience with Docker, Kubernetes for containerization
- Strong software engineering practices (clean code, testing, documentation)
- Databricks, Azure ML, ADF, Web Apps
- Experience with distributed computing and big data processing
- Infrastructure as Code (Terraform, ARM templates)
- Hands-on experience with foundation models: GPT-4, Claude, Gemini
- LLM frameworks: LangChain, LlamaIndex, LangGraph
- Vector databases: Pinecone, Chroma, pgvector
- Fine-tuning techniques: LoRA, QLoRA, PEFT
- Hugging Face ecosystem (Transformers, Datasets, Hub)
- Embedding models and semantic search implementation
- Deep learning frameworks: TensorFlow, PyTorch, JAX
- Classical ML: scikit-learn, XGBoost, LightGBM, Regression and Classification
- Strong expertise in NLP, Time Series Forecasting
- Experience with recommendation systems and reinforcement learning
- Solid understanding of model evaluation, optimization, bias mitigation, and monitoring.
- MLflow, Weights & Biases for experiment tracking
- GitHub Actions, Azure DevOps for CI/CD
- Model monitoring and A/B testing frameworks
Required
- 10+ years of hands-on experience delivering enterprise-grade AI/ML solutions
- Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or related quantitative field
- Proven track record of deploying ML models in production at scale
- Strong business acumen and ability to bridge the gap between data and decisions
- Experience leading cross-functional AI initiatives
- PhD in relevant field
- Prior understanding of shipping and logistics domain
- Open source contributions to ML projects
- Experience with agentic workflows and autonomous systems
- Opportunity to work on cutting-edge AI projects at global scale
- Access to state-of-the-art computing resources and tools
- Collaborative environment with top AI/ML talent
- Professional development and conference attendance support
- Competitive compensation and benefits package
We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [email protected].
Required Skills
Machine Learning
Engineering
Logistics
Forecasting
Documentation
Leadership
Safety
Monitoring
Python
SQL
Training
Sustainability
Docker
Kubernetes
Terraform
GitHub
Deep Learning
Computer Vision
TensorFlow
PyTorch
Scikit-learn
MLOps
Supply Chain Optimization
Storytelling
Reinforcement Learning
Recommendation Systems
Knowledge Management
Azure
Azure DevOps
GitHub Actions
XGBoost
LightGBM
LangChain
MLflow
Databricks
Drift
A/B Testing
Data Engineering
DevOps
CI/CD
Mentoring
Performance Tracking
Testing
Statistics
NLP
Analytics
Supply chain
RAG
Validation
ADF
Regression
Cost optimization
GPT
Software engineering
Prediction
Business acumen
Bias mitigation
Model development
Data processing
Vector
Customer solutions
Big Data
Art
Generative
Semantic
Guardrails
Model Evaluation
Pinecone
Chroma
Transformers
Data systems
ARM templates
Solution architecture
Data Exploration
Testing frameworks
PySpark
Shipping
Embedding
Semantic search
Data generation
Synthetic
Fine-tuning
Synthetic Data
Time series forecasting
Production Engineering
Clean code
Demand
Model monitoring
Detection
Experiment
Reproducibility
Exploration
Inference
Distributed computing
GenAI
Prompt engineering
Gemini
Deep learning frameworks
Machine Learning and
Retrieval
Infrastructure as Code
Model Training
Detection systems
Observability
Engineering practices
Rollback
Generative AI
Computing
Classification
GenAI capabilities
Big Data processing
Shipping and logistics
Compensation and Benefits
Azure ML
Professional Development
AI/ML
Computer Science
SciKit
LLM
LoRA
PEFT
Augmented Generation
Vector Databases
Langgraph
Claude
Feature Engineering
For containerization
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