Ai Ml Engineer

Role & responsibilities

Machine Learning / AI Engineer

Key Responsibilities

1. Machine Learning & Deep Learning Development

• Design, develop, and optimize ML/DL models for classification, regression, NLP, computer vision, and generative tasks.
  
• Implement neural network architectures such as CNNs, RNNs, Transformers, and autoencoders.
  
• Use frameworks such as

  TensorFlow

  ,

  Keras

  , and

  PyTorch

  for model training and experimentation.

2. Data Preprocessing & Feature Engineering

• Collect, clean, and transform structured and unstructured data for model training.
  
• Perform feature extraction, feature selection, normalization, augmentation, etc.
  
• Work with large-scale datasets and modern data formats (Parquet, Avro, TFRecord).
  

3. NLP & Large Language Models

• Develop NLP pipelines for tasks such as text classification, summarization, NER, embeddings, and conversational AI.
  
• Fine-tune or deploy LLMs (GPT, Llama, Mistral, etc.).
  
• Evaluate model performance using perplexity, BLEU, ROUGE, and other metrics.
  

4. Generative AI & API Integration

• Build applications using

  OpenAI

  ,

  Google Gemini

  ,

  Hugging Face

  ,

  Anthropic Claude

  , and similar APIs.
• Implement generative workflows: text-to-text, text-to-image, embeddings, RAG pipelines, and multi-agent systems.
  
• Optimize cost, latency, and performance when integrating cloud-based GenAI APIs.
  

5. MLOps / AIOps

• Create scalable ML pipelines and automate model training, validation, deployment, and monitoring.
  
• Use CI/CD tools for ML (GitHub Actions, GitLab CI, Jenkins, etc.).
  
• Manage model versioning, feature stores, and experiment tracking via MLflow, DVC, Weights & Biases.
  
• Deploy models on cloud platforms (AWS/GCP/Azure), Kubernetes, and serverless environments.
  
• Implement observability, logging, drift detection, and automated retraining workflows.
  

6. Prompt Engineering & LLM Optimization

• Design, test, and optimize prompts for LLM performance improvements.
  
• Build structured prompting, chain-of-thought, function calling, and agent workflows.
  
• Develop evaluation frameworks for prompt reliability and model output quality.
  

7. AI-Powered Development Tools

• Use tools like

  GitHub Copilot

  ,

  Cursor

  ,

  Claude

  , and other AI assistants to enhance coding productivity.
• Implement AI-assisted automation in software development workflows.
  

8. Collaboration & Documentation

• Work closely with data engineers, software engineers, and product teams to define project requirements.
  
• Create technical documentation, reports, and model explainability summaries.
  
• Contribute to code reviews and architectural discussions.
  

Required Skills & Qualifications

Core Technical Skills

• Strong knowledge of ML/DL concepts: supervised/unsupervised learning, optimization, regularization, loss functions, probabilistic models.
  
• Proficiency in

  TensorFlow

  ,

  PyTorch

  ,

  Keras

  , and Scikit-Learn.
• Solid knowledge of NLP, LLMs, Transformers, embeddings, and attention mechanisms.
  
• Experience using GenAI APIs (OpenAI, Gemini, Hugging Face, Claude).
  
• Familiarity with containerization (Docker), orchestration (Kubernetes), and cloud deployment.
  

Programming Skills

• Strong Python skills (NumPy, Pandas, Matplotlib, FastAPI/Flask).
  
• Knowledge of SQL and NoSQL databases.
  
• Experience in building REST APIs and AI microservices.
  

MLOps & Deployment

• Hands-on with MLflow, DVC, W&B, Airflow, Kubeflow.
  
• Understanding of CI/CD, Git, version control, and DevOps principles.
  

Soft Skills

• Analytical thinking and problem-solving.
  
• Strong communication skills and ability to explain complex concepts.
  
• Ability to work in cross-functional, agile environments.
  

Preferred candidate profile