Machine Learning Engineer

We are seeking a highly technical Machine Learning Engineer with strong expertise in classical Machine Learning, Deep Learning, and Generative AI systems (LLMs & foundation models). The ideal candidate must have hands-on experience designing end-to-end ML pipelines, implementing LLM-powered systems, optimizing models for production, and deploying scalable AI solutions using modern MLOps practices. This role requires strong mathematical foundations, solid programming skills, system design thinking, and production deployment experience. 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