Experience: 2-5 years
Location: Bangalore, INDIA
Qualification: Bachelors
Job Overview:
We are seeking a mid-level AI Engineer with 2–5 years of experience to design, develop, deliver, manage, and optimize ML and GenAI integrations into our product roadmap. The ideal candidate will lead AI use case deployments end-to-end, including exploratory data analysis (EDA), data engineering, model training, and production optimization. This role also involves mentoring and guiding junior AI engineers to ensure high-quality delivery and team growth.
Key Responsibilities:
AI Model Development & Deployment:
- Design and develop ML and GenAI models for airport retail use cases such as demand forecasting, personalized recommendations, anomaly detection in passenger flows, and real-time store optimization.
- Conduct exploratory data analysis (EDA), build data pipelines, and perform data engineering to prepare high-quality datasets from diverse sources.
- Deploy, monitor, and optimize AI models in production environments, ensuring scalability, low latency, and reliability.
- Implement MLOps best practices for model lifecycle management and continuous improvement.
Collaboration & Roadmap Alignment:
- Collaborate with product managers, architects, and cross-functional teams to align AI initiatives with business goals.
- Integrate AI solutions seamlessly into product roadmaps and enterprise systems.
- Contribute to strategic planning by evaluating emerging AI and GenAI technologies.
Mentorship & Innovation:
- Mentor 2–5 junior AI engineers or freshers through hands-on training, code reviews, and project guidance.
- Drive knowledge transfer and improve team capabilities and delivery timelines.
- Experiment with emerging GenAI techniques such as LLM-based retail insights and advanced prompt engineering.
Experience Requirements:
- 3–5 years of experience in AI/ML engineering with a proven track record of deploying and managing at least 3 production AI use cases.
- Hands-on experience in EDA, data engineering, model training, and MLOps for real-world applications.
Mandatory Skills:
- Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Strong data engineering skills including SQL, Pandas, ETL pipelines, and tools like Apache Spark or Airflow.
- Experience in end-to-end AI deployment using Docker, Kubernetes, MLflow/CML, AWS SageMaker, or Azure ML.
- Advanced prompt engineering skills and ability to identify suitable model variants including SLMs.
- Experience with GenAI/LLMs including fine-tuning models such as Llama or GPT variants.
- Strong EDA expertise including feature engineering and handling imbalanced or multimodal datasets.
- Proven mentorship experience guiding junior engineers through at least two AI projects.
Good-to-Have Skills:
- Experience working with cloud platforms such as AWS.
- Familiarity with monitoring tools like Prometheus, Grafana, or Datadog for model performance tracking.
- Experience with advanced GenAI implementations such as RAG pipelines, agentic AI, or multimodal models.
