Machine Learning Engineer (Generative AI & ML Specialist)

Roles & Responsibilities

1.

Machine Learning Development

• Design and implement ML solutions for:
  • Classification, regression, clustering
  • Anomaly detection
  • Recommendation systems
  • Time-series forecasting
• Perform feature engineering and advanced data preprocessing.
• Implement
  • Cross-validation strategies
  • Hyperparameter tuning (Grid, Random, Bayesian)
  • Model evaluation pipelines
• Apply ensemble methods such as Boosting, Bagging, and Stacking.
• Optimize models for inference speed, memory usage, and scalability.
2.

Deep Learning & NLP

• Develop and fine-tune deep learning models:
  • CNNs, RNNs, LSTMs
  • Transformer-based architectures
• Build NLP pipelines:
  • Tokenization
  • NER
  • Semantic similarity
  • Text classification
• Fine-tune pretrained transformer models for domain-specific applications.
• Implement attention mechanisms and embedding pipelines.
3.

Generative AI & LLM Engineering

• Integrate Large Language Models into applications.
• Build:
  • Retrieval-Augmented Generation (RAG) systems
  • Prompt chaining pipelines
  • Multi-step reasoning workflows
• Perform:
  • Supervised fine-tuning
  • LoRA / QLoRA-based parameter-efficient fine-tuning
  • Instruction tuning
• Work with:
  • Embeddings
  • Vector similarity search
  • Context management strategies
• Implement:
  • Hallucination mitigation techniques
  • Output validation & guardrails
  • Token usage optimization
• Optimize LLM inference using:
  • Quantization
  • Model distillation
  • Caching strategies
4.

MLOps & Production Deployment

• Deploy models using REST APIs (FastAPI / Flask).
• Containerize ML services using Docker.
• Implement CI/CD pipelines for ML workflows.
• Use model tracking and versioning tools.
• Implement:
  • Model monitoring
  • Drift detection
  • Automated retraining pipelines
• Deploy scalable AI systems on cloud infrastructure.
5.

Data Engineering & Infrastructure

• Build ETL pipelines for large datasets.
• Handle structured & unstructured data.
• Work with distributed systems when required.
• Design scalable data storage architectures.

Tools & Technology Stack (Required / Preferred)

Programming & Core Libraries

• Python (mandatory)
• NumPy
• Pandas
• SciPy
• Scikit-learn

Deep Learning Frameworks

• PyTorch
• TensorFlow / Keras

NLP & LLM Ecosystem

• Hugging Face Transformers
• Sentence Transformers
• Tokenizers
• LangChain or LLM orchestration frameworks
• OpenAI / Anthropic / other LLM APIs
• RAG pipeline frameworks

Vector Databases

• FAISS
• Pinecone
• Weaviate
• Chroma (preferred)

Databases

• PostgreSQL
• MySQL
• MongoDB
• Redis

MLOps & Experiment Tracking

• MLflow
• Weights & Biases
• DVC (preferred)

Deployment & Infrastructure

• FastAPI / Flask
• Docker
• Kubernetes (preferred)
• Nginx (preferred)
• REST / gRPC services

Cloud Platforms

• AWS (SageMaker, EC2, S3)
• Azure ML
• Google Cloud AI Platform

Data & Distributed Processing

• Apache Spark (preferred)
• Airflow (preferred)

Version Control & Collaboration

• Git (branching strategies, pull requests, code reviews)
• GitHub / GitLab / Bitbucket

Required Technical Knowledge

• Strong understanding of:
  • Linear algebra
  • Probability & statistics
  • Optimization algorithms
• Deep knowledge of:
  • Transformer architecture
  • Attention mechanisms
  • Embedding models
• Experience with:
  • Model quantization
  • Inference optimization
  • Latency & cost optimization in LLM systems
• Understanding of Responsible AI:
  • Bias mitigation
  • Fairness
  • Model explainability

Soft Skills

• Strong analytical thinking.
• Clear documentation practices.
• Ability to translate business problems into ML solutions.
• Research-oriented and experimentation mindset.
• Strong English communication skills.

Preferred Qualifications

• Experience with multimodal AI (text + image/audio).
• Experience with diffusion models.
• Distributed training (multi-GPU).
• Experience building production-scale AI SaaS products.
• Contributions to open-source or research publications.

Ideal Candidate Profile

• Strong ML theoretical foundation.
• Production-first mindset.
• Deep hands-on experience with GenAI.
• System design and scalability oriented.
• Passionate about cutting-edge AI technologies.

What we offer you

• Flexible Working

• Competitive Compensation

• Insurance Benefits

• Training & Mentoring

• Frequent Celebrations

• Home Office Allowance

• Paid Leave Benefits

• Retirement Benefits

• Partial Course Funding

• Team Building Activities