AI/ML Engineer

About Ivtex Corporate Solutions Private Limited

Ivtex Corporate Solutions Private Limited excels in delivering advanced, technology-driven solutions tailored to meet dynamic business requirements. Specializing in Artificial Intelligence, Data Analytics, Consulting, Emerging Technologies, and Cybersecurity, Ivtex helps organizations across sectors remain competitive in the digital era. They focus on building robust, scalable, and forward-looking solutions emphasizing efficiency, innovation, and measurable results, creating an engaging work environment for technology enthusiasts.

Position Overview

As an AI/ML Engineer, you will be accountable for designing, creating, deploying, and maintaining AI and ML solutions addressing real-world business challenges. This role entails managing the entire machine learning lifecycle including data handling, model building, deployment, API integration, monitoring, and optimization. Projects may involve Generative AI, Large Language Models (LLMs), computer vision, natural language processing (NLP), and intelligent automation based on needs.

Key Responsibilities

• Develop, train, validate, and deploy machine learning and deep learning models.
• Preprocess and analyze both structured and unstructured datasets for model training.
• Implement data cleaning, feature engineering, selection, and exploratory data analysis techniques.
• Create predictive, classification, recommendation, and forecasting models along with other AI/ML applications.
• Use Python and frameworks like Scikit-learn, TensorFlow, PyTorch, and XGBoost for solution development.
• Build and integrate Generative AI and LLM-powered applications when necessary.
• Utilize LLM APIs and open-source solutions for chatbots, document processing, summarization, information extraction, and knowledge assistants.
• Implement Retrieval-Augmented Generation (RAG) workflows, embeddings, vector search, and prompt engineering relevant to Generative AI.
• Develop NLP and computer vision capabilities aligned with project goals.
• Create RESTful APIs using FastAPI or Flask to integrate AI/ML models into applications.
• Deploy machine learning models into live production environments interfacing with web, mobile, or backend systems.
• Optimize models focussing on accuracy, latency, scalability, and computational efficiency.
• Define model evaluation metrics and execute validation, testing, and benchmarking activities.
• Continuously monitor performance to detect model degradation, data drift, and related issues in production.
• Keep thorough version control, experiment records, documentation, and ensure reproducibility of models.
• Collaborate effectively with software developers, data engineers, business analysts, QA personnel, and project managers.
• Translate business requirements into practical and effective AI/ML solutions.
• Stay updated on emerging AI/ML technologies and assess their applicability to current projects.
• Adhere to data privacy, security, and ethical AI standards in all tasks.

Required Skills and Knowledge

• Programming in Python and SQL.
• Machine learning libraries and techniques including Scikit-learn, Pandas, NumPy, XGBoost/LightGBM, supervised and unsupervised learning, feature engineering, and model evaluation.
• Deep learning using PyTorch and/or TensorFlow/Keras; knowledge of neural networks, CNNs, and transformers.
• Hands-on experience with Generative AI and LLMs including prompt engineering, embeddings, RAG pipelines, vector databases, and API integration.
• Expertise in NLP tasks such as text preprocessing, classification, information extraction, semantic search, and transformer models.
• Preferred skills in computer vision: OpenCV, object detection, image classification, OCR and related models.
• API development skills using FastAPI, Flask, and RESTful services.
• Database management with MySQL/PostgreSQL and familiarity with NoSQL types.
• Deployment and MLOps practices including Docker, Git, CI/CD pipelines, model versioning, experiment tracking, and monitoring.
• Familiarity with cloud platforms like AWS, Microsoft Azure, or Google Cloud is preferred.

Additional Tool Experience Advantageous

• Knowledge of Hugging Face Transformers, LangChain, LlamaIndex, or similar frameworks.
• Work with vector search engines like FAISS, Pinecone, Weaviate, or Chroma.
• Experience in MLflow, Weights & Biases for experiment tracking.
• Use of Jupyter Notebooks and version control systems such as Git/GitHub/GitLab.
• Containerization with Docker and orchestration using Kubernetes.
• Workflow automation using Apache Airflow.
• Computer vision model optimization with ONNX.
• Handling cloud-based AI/ML services.

Knowledge Foundations

• Comprehensive understanding of machine learning algorithms and theory.
• Strong foundation in statistics, probability, and linear algebra.
• Skilled in data preprocessing, feature engineering, model selection, and hyperparameter tuning.
• Deep learning architectures including transformers and related modern AI models.
• Proficient in evaluating model performance using various metrics.
• Data structures, algorithms, and production deployment workflows.
• Data privacy, security best practices, and responsible AI principles.

Competencies

• Excellent analytical thinking and problem-solving skills.
• Independent troubleshooting capability across technical issues.
• Strong programming, debugging, and documentation abilities.
• Research-driven mindset with eagerness to explore cutting-edge AI advancements.
• Business problem understanding and AI solution translation skills.
• Effective communication and teamwork across functions.
• Time and task management to handle multiple projects and deadlines.

Preferred Profile

• Proven experience delivering a production-level AI/ML solution from start to finish.
• Familiarity with developing projects encompassing data prep, model building, deployment, and monitoring.
• Exposure to GenAI/LLM projects utilizing RAG or comparable architectures.
• Experience integrating AI models within software products.
• Comfort working with extensive datasets and enterprise databases.
• A portfolio demonstrating AI/ML work such as on GitHub.
• Background in government, enterprise, smart-city, surveillance, analytics, or complex tech environments is a plus.

Selection Process

• Screening of resumes and profiles.
• Technical interviews covering AI/ML concepts.
• Practical assignments or coding tests related to AI/ML.
• Presentation of projects or model demonstrations.
• Final discussions involving technical and managerial aspects.

Expectations from Selected Candidate

The appointed AI/ML Engineer will be responsible for delivering reliable AI solutions ready for production, maintaining detailed technical documentation, supporting deployment and integration, monitoring live model performance, and iteratively enhancing AI/ML applications aligned with business requirements.