AI/ML Engineer

Company Description Ivtex Corporate Solutions Private Limited delivers high-quality, technology-driven solutions tailored to clients’ evolving business needs. The organization specializes in AI, Data Analytics, Consulting, Emerging Technologies, and Cybersecurity, helping companies stay competitive in an increasingly digital world. Ivtex collaborates with organizations across diverse industries to design robust, scalable, and future-ready solutions. Its focus on efficiency, innovation, and measurable outcomes creates a dynamic environment for professionals interested in advanced technology and applied AI.
Job Description – AI/ML Engineer
Position: AI/ML Engineer
Department: Technology / AI & Data Science
Employment Type: Full-Time
Location: Bhubaneswar, Odisha
Reporting To: Technical Lead / Project Manager
Experience: 2–5 Years
Qualification: B.Tech/B.E./M.Tech/MCA/M.Sc. in Computer Science, IT, Data Science, Artificial Intelligence, Machine Learning, or related field
Role Overview
We are looking for a skilled and motivated AI/ML Engineer to design, develop, deploy, and maintain Artificial Intelligence and Machine Learning solutions for real-world business applications.
The candidate will be responsible for the complete ML lifecycle, including data preparation, model development, evaluation, deployment, API integration, performance monitoring, and optimization. The role may also involve Generative AI, Large Language Models (LLMs), computer vision, NLP, and intelligent automation depending on project requirements.
Key Responsibilities
Design, develop, train, test, and deploy Machine Learning and Deep Learning models.
Analyze and preprocess structured and unstructured datasets for model development.
Perform data cleaning, feature engineering, feature selection, and exploratory data analysis.
Develop predictive models, classification models, recommendation systems, forecasting solutions, and other ML-based applications.
Build solutions using Python and ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or equivalent technologies.
Develop and integrate Generative AI and LLM-based applications, where required.
Work with LLM APIs and open-source models for applications such as chatbots, document intelligence, summarization, information extraction, and knowledge assistants.
Implement RAG (Retrieval-Augmented Generation) pipelines, embeddings, vector search, and prompt engineering for GenAI applications.
Work on NLP and/or Computer Vision solutions based on project requirements.
Develop REST APIs using frameworks such as FastAPI or Flask for AI/ML model integration.
Deploy ML models into production environments and integrate them with web/mobile/backend applications.
Optimize models for accuracy, latency, scalability, and computational efficiency.
Establish model evaluation metrics and conduct validation, testing, and performance benchmarking.
Monitor deployed models for performance degradation, data drift, model drift, and other production issues.
Maintain proper model versioning, experiment tracking, documentation, and reproducibility.
Collaborate with software developers, data engineers, business analysts, QA teams, and project managers.
Convert business requirements into technically feasible AI/ML solutions.
Research emerging AI/ML technologies and evaluate their applicability to organizational projects.
Follow data privacy, information security, and responsible AI practices.
Required Technical Skills
Programming:
Python, SQL
Machine Learning:
Scikit-learn, Pandas, NumPy, XGBoost/LightGBM, feature engineering, supervised and unsupervised learning, model evaluation and optimization
Deep Learning:
PyTorch and/or TensorFlow/Keras, neural networks, CNNs, Transformers
Generative AI / LLM:
LLMs, prompt engineering, embeddings, RAG, vector databases, LLM APIs and/or open-source models
NLP:
Text preprocessing, classification, information extraction, semantic search, transformers
Computer Vision – Preferred:
OpenCV, object detection, image classification, OCR and related vision models
API Development:
FastAPI / Flask / REST API integration
Databases:
MySQL/PostgreSQL and exposure to NoSQL databases
MLOps / Deployment:
Docker, Git, CI/CD fundamentals, model versioning, experiment tracking and production model monitoring
Cloud – Preferred:
AWS / Microsoft Azure / Google Cloud Platform
Preferred Tools & Technologies
Exposure to some of the following will be advantageous:
Hugging Face Transformers
LangChain / LlamaIndex or similar frameworks
FAISS / Pinecone / Weaviate / Chroma or other vector databases
MLflow / Weights & Biases
Jupyter Notebook
Git/GitHub/GitLab
Docker
Kubernetes
Apache Airflow
OpenCV
ONNX
Cloud-based AI/ML services
Required Knowledge
The candidate should have a strong understanding of:
Machine Learning algorithms
Statistics and probability
Linear algebra fundamentals
Data preprocessing and feature engineering
Model selection and hyperparameter tuning
Classification, regression and clustering
Deep Learning architectures
Transformers and modern AI architectures
Model evaluation metrics
Data structures and algorithms
ML deployment and inference pipelines
API-based model integration
Data security and privacy principles
Key Competencies
Strong analytical and problem-solving ability
Ability to independently troubleshoot technical issues
Strong coding and debugging skills
Research-oriented approach to emerging AI technologies
Ability to understand business problems and translate them into AI solutions
Good documentation skills
Effective communication and cross-functional collaboration
Ability to manage multiple development tasks and project deadlines
Preferred Candidate Profile
Preference will be given to candidates who have:
Developed and deployed at least one production-level AI/ML application.
Hands-on experience taking an ML project from data preparation → model development → deployment → monitoring.
Experience developing GenAI/LLM applications using RAG or similar architectures.
Experience integrating AI models with existing software applications.
Experience working with large datasets and production databases.
A strong GitHub portfolio or demonstrable AI/ML projects.
Experience working on government, enterprise, smart-city, surveillance, analytics, or large-scale technology projects will be an added advantage.
Selection Process
Candidates may be evaluated through:
Resume/Profile Screening
Technical Interview
Practical AI/ML Assignment or Coding Test
Project/Model Demonstration
Final Technical/Management Discussion
During the technical evaluation, candidates should be prepared to explain one AI/ML project end-to-end, including the business problem, dataset, preprocessing, model selection, evaluation metrics, deployment architecture, challenges faced, and final results.
Expected Deliverables
The selected AI/ML Engineer will be expected to deliver reliable and production-ready AI solutions, maintain appropriate technical documentation, support deployment and integration, monitor model performance, and continuously improve AI/ML systems based on business and project requirements.

Work arrangement
No