Python - AI / ML Engineer

Role Summary & Key Objectives

• Translate business problems into ML/AI solutions with measurable impact.
• Build and optimize production-grade ML models that are scalable and reliable.
• Ensure robustness, fairness, and efficiency in ML pipelines.
• Drive adoption of AI/ML best practices across the organization.

Core Responsibilities

• Design and implement ML algorithms for prediction, classification, recommendation, NLP, or computer vision use cases.
• Collect, clean, and preprocess large datasets for model development.
• Develop, train, validate, and fine-tune machine learning / deep learning models.
• Deploy ML models into production using MLOps best practices (CI/CD pipelines, monitoring, retraining).
• Collaborate with cross-functional teams (data engineers, product managers, software developers) to integrate AI features into products.
• Continuously evaluate new ML techniques, frameworks, and research to improve model accuracy and performance.
• Document solutions, conduct knowledge-sharing sessions, and mentor junior engineers.

Must-Have Skills (Technical & Soft)

• Strong programming skills in

  Python

  (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Experience with

  machine learning algorithms

  (supervised, unsupervised, reinforcement learning).
• Hands-on expertise in

  deep learning

  (CNNs, RNNs, Transformers).
• Proficiency in

  data preprocessing, feature engineering, and statistical analysis

  .
• Experience with

  SQL/NoSQL databases

  and handling large datasets.
• Familiarity with

  MLOps tools

  (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
• Knowledge of cloud platforms (AWS Sagemaker, Azure ML, GCP AI Platform).

• Generative AI models

  (e.g., LLMs, GANs, Diffusion Models, VAEs, Transformers).
• Fine-tune and optimize

  foundation models

  for domain-specific applications

• Agentic AI framework

  (AutoGen, LangChain/LangGraph, CrewAI, and Microsoft Semantic Kernel, OpenAI)
• Experience in

  multimodal AI

  (text, image, audio, video generation).
• Familiarity with prompt engineering & fine-tuning LLMs.
• Knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus etc) for retrieval-augmented generation (RAG)

• Strong problem-solving and analytical thinking.
• Excellent communication and presentation skills.
• Ability to work in collaborative, cross-functional teams.
• Self-driven and proactive in exploring new technologies.

Good-to-Have Skills

• Exposure to

  NLP frameworks

  (Hugging Face, spaCy, NLTK).
• Computer Vision experience (OpenCV, YOLO, Detectron).
• Experience with

  big data frameworks

  (Spark, Hadoop).
• Knowledge of

  generative AI

  (LLMs, diffusion models, prompt engineering).
• Contribution to research papers, open-source projects, or Kaggle competitions.
• Familiarity with

  A/B testing and experimentation frameworks

  .

Experience Requirements

• 4 to 8 years

  of professional experience in AI/ML development.
• Proven track record of building and deploying ML models into production.
• Experience in solving business problems through applied machine learning.

KPIs / Success Metrics

• Accuracy, precision, recall, F1-score, or other relevant model performance metrics.
• Successful deployment of ML models with minimal downtime and robust monitoring.
• Reduction in data processing or inference time (efficiency improvements).
• Measurable business impact (e.g., improved predictions, reduced churn, better personalization).
• Contribution to team learning through code reviews, mentoring, and documentation.
• Staying updated and adopting relevant cutting-edge ML/AI techniques.