Responsibilities
- Design, develop, and validate machine learning and deep learning models to address domain-specific business challenges
- Work across a range of approaches including classical ML, NLP, computer vision, reinforcement learning, and transformer-based models
- Apply Generative AI techniques, including working with large language models and retrieval-based systems, where appropriate
- Collaborate with data scientists and engineers to source, clean, and preprocess large datasets
- Perform feature engineering and data selection to improve model inputs and outcomes
- Deploy models into production environments that support real-time or near real-time use cases
- Build and maintain MLOps pipelines for deployment, monitoring, versioning, and retraining
- Use Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS, Azure, or GCP
- Integrate models into applications through APIs or model-serving frameworks
- Optimize models for performance, latency, scalability, and resource efficiency
- Implement testing strategies including unit testing, regression testing, and A/B testing
- Monitor model performance and improve solutions based on data, feedback, and usage patterns
- Work closely with cross-functional teams including engineering, product, and subject matter experts
- Document model architectures, training processes, and experimental results
- Communicate technical concepts clearly to both technical and non-technical stakeholders
- Contribute to ethical AI practices with attention to fairness, transparency, and accountability
- Help identify and mitigate risks such as bias, hallucinations, or incorrect outputs
Requirements
- Minimum 2 years of hands-on experience in AI or machine learning development
- Strong proficiency in Python and experience with libraries such as TensorFlow, PyTorch, Keras, or Scikit-learn
- Solid understanding of ML and DL architectures including CNNs, RNNs or LSTMs, and Transformers
- Experience with data manipulation and analysis using Pandas, NumPy, and SciPy
- Familiarity with software engineering best practices including Git, testing, and code optimization
- Experience deploying models to cloud platforms or on-premise environments
- Ability to work full-time from our Noida office
Nice to Have
- Hands-on experience with large language models such as GPT, Claude, Mistral, or LLaMA
- Experience with prompt engineering, Retrieval-Augmented Generation (RAG), or vector-based search
- Familiarity with tools such as LangChain, LlamaIndex, Hugging Face, Pinecone, FAISS, or Weaviate
- Experience with MLOps tools like MLflow, Kubeflow, or DVC
- Exposure to applying AI in domains such as healthcare, finance, retail, manufacturing, or customer service
- Strong grounding in statistics and optimization
Benefits
- Work on real, production-grade AI systems with measurable business impact
- Collaborate with experienced engineers and AI practitioners in a supportive, hands-on environment
- Build breadth across models, tools, and deployment approaches while deepening your expertise
- Be part of a team that values responsible, ethical AI and high-quality engineering
Work Arrangement
On-site
