MLOps Engineer
1 Position
Scalable AI Systems & Automation
Location: [Remote / Hybrid / On-site – specify location]
Type: Full-time
Experience Required: 5+ years
Department: AI Engineering & Model Operations
Engineer the Future of AI in Production
If you are passionate about making AI real, robust, and resilient, and thrive in a high-impact, fast-paced environment, we’d love to have you on board.
Apply now to operationalize the intelligence of tomorrow.
We are hiring a forward-thinking MLOps Engineer to bridge the gap between machine learning development and production-scale deployment. You’ll work closely with data scientists, engineers, and architects to build, automate, monitor, and scale AI/ML models in real-time environments.
This role is ideal for someone who thrives in infrastructure-driven innovation, understands the nuances of model lifecycle management, and can help operationalize next-generation AI architectures, including LLMs and AI agents.
🔁 Model Lifecycle Automation
- Build and manage end-to-end ML pipelines — from data validation and model training to testing and deployment.
- Automate CI/CD workflows for ML models using GitOps, Jenkins, GitHub Actions, ArgoCD, or similar.
- Implement versioning, packaging, and reproducibility standards (using MLflow, DVC, or BentoML).
🚀 Deployment & Scaling
- Design scalable, secure, and resilient model deployment strategies (batch, streaming, real-time).
- Containerize models using Docker/Kubernetes, and deploy on cloud-native services (SageMaker, Azure ML, GCP Vertex AI).
- Support API-first deployment of LLMs, RAG pipelines, and AI agents via REST/gRPC endpoints.
📊 Monitoring & Observability
- Set up monitoring systems for models (performance, drift, latency, data anomalies) using Prometheus, Grafana, Evidently, or WhyLabs.
- Build alerts and dashboards for real-time visibility into model behavior, failures, and throughput.
- Enable explainability and traceability in production environments.
🔐 Security, Governance, and Compliance
- Integrate role-based access control (RBAC) and authentication/authorization using OAuth2, JWT, or Azure AD.
- Ensure compliance with data governance, auditability, and AI ethics standards (e.g., GDPR, HIPAA, SOC2).
- Implement zero-trust principles and secrets management for sensitive model and data access.
🤝 Collaboration & System Integration
- Work closely with data engineers, AI developers, and DevOps teams to align on architecture, model interfaces, and system health.
- Drive adoption of MLOps best practices and maintain documentation/playbooks for model deployment processes.
- Support multi-modal AI systems and cross-functional AI platform integration.
- Bachelor’s/Master’s in Computer Science, Engineering, or related field.
- 5+ years of experience in DevOps, MLOps, or Machine Learning infrastructure roles.
- Deep experience with CI/CD pipelines, model versioning, and orchestration tools.
- Solid knowledge of cloud platforms (AWS/GCP/Azure) and container orchestration (K8s, Helm).
- Strong skills in Python, Shell scripting, YAML, and API integration.
- Exposure to LLMOps or operationalizing large language models (fine-tuning, RAG, vector stores).
- Familiarity with LangChain, AutoGen, or agent-based orchestration frameworks.
- Hands-on with multi-cloud or hybrid cloud infrastructure.
- Experience with monitoring tools and SRE principles.
- Understanding of data drift, model fairness, and explainable AI tools.
- Opportunity to own the MLOps function in a cross-functional AI-first environment.
- Collaborate on deploying LLMs, AI agents, and future-ready GenAI systems.
- Be part of a team where AI is not just experimental—but operational, scalable, and strategic.
- Enable real-time intelligence with robust infrastructure, transforming ideas into production models.
- Competitive salary + model-performance incentives
- Work-from-anywhere flexibility
- Training and certification support for MLOps & GenAI tools
- Access to AI R&D and internal product development
- Wellness, upskilling, and innovation perks
Junior Data Scientist
1 Position
Applied AI & Emerging Technologies
Location: [Remote / Hybrid / On-site – specify location]
Type: Full-time
Experience Required: 4+ years
Department: AI, Data Science & Innovation
We are looking for a dynamic Junior Data Scientist who is ready to evolve from traditional data modeling into next-generation AI and intelligent agent development.
You will be working alongside senior experts to design, deploy, and optimize cutting-edge AI systems — contributing to real-world solutions while strengthening your skills across conventional and futuristic AI domains.
This role is perfect for those who are technically hands-on, curious about AI’s future, and passionate about building production-ready applications.
✅ Core Data Science & Applied AI
- Develop predictive, classification, clustering, and recommendation models.
- Fine-tune and apply pre-trained LLMs for domain-specific use-cases.
- Support the building and orchestration of AI Agents using frameworks like LangChain, AutoGen, or similar.
- Apply core NLP, computer vision, and statistical modeling techniques to solve business challenges.
⚙️ AI System Development & Support
- Participate in the development of end-to-end ML pipelines from data preprocessing to model deployment.
- Implement data ingestion, feature engineering, model evaluation, and deployment pipelines.
- Work with APIs and integrate authentication protocols (OAuth2, SSO) into AI workflows.
- Collaborate with MLOps teams to containerize, monitor, and optimize models.
🔐 MLOps and Best Practices (Learning and Implementation)
- Assist in versioning models and datasets using tools like MLflow or DVC.
- Monitor model performance and flag drifts, biases, and fairness issues.
- Follow and contribute to CI/CD pipelines for machine learning.
🌐 Learning and Innovation
- Stay updated on latest AI research, LLM fine-tuning techniques, prompt engineering, and AI security practices.
- Explore and implement emerging techniques in agentic AI, multi-modal learning, and real-world orchestration.
- Actively participate in brainstorming sessions, innovation initiatives, and hackathons.
- Bachelor's/Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or related fields.
- 4+ years of relevant hands-on experience in data science, machine learning, and AI applications.
- Strong programming skills in Python and libraries like Scikit-learn, TensorFlow, PyTorch.
- Experience working with LLM APIs (OpenAI, Hugging Face Transformers, etc.) preferred.
- Good understanding of SQL, data pipelines, and cloud environments (AWS, Azure, or GCP).
- Exposure to vector databases (Pinecone, FAISS) and retrieval-augmented generation (RAG) techniques.
- Basic experience with MLOps platforms (SageMaker, MLflow, Vertex AI).
- Familiarity with API integrations, secured deployments, and containerization (Docker, Kubernetes).
- Understanding of AI security, bias, and ethical practices.
- Interest in multi-modal AI (combining text, image, video, and audio).
- Accelerated learning environment working directly with senior AI leaders.
- Opportunities to work on next-gen AI architectures (agentic systems, autonomous AI).
- Hands-on exposure to real-world AI deployment, scalability, and optimization challenges.
- Culture of experimentation, ownership, and ethical innovation.
- Competitive salary + learning incentives
- AI research and upskilling sponsorship
- Mentorship under senior AI architects and data science leaders
- Dynamic, innovation-driven environment
Data Engineer / Data Architect
1 Position
AI Infrastructure & Scalable Pipelines
Location: [Remote / Hybrid / On-site – specify location]
Type: Full-time
Experience Required: 6+ years
Department: Data & AI Engineering
We are seeking a highly skilled Data Engineer / Architect to design, develop, and optimize scalable data infrastructure and AI-ready pipelines that support the development and deployment of next-generation intelligent systems. You will play a critical role in enabling Senior and Junior Data Scientists by ensuring clean, secure, structured, and accessible data, while also aligning with MLOps, agentic AI frameworks, and enterprise-grade performance.
This role demands technical excellence, system-level thinking, and a passion for future-ready architectures.
🏗️ Data Architecture & Infrastructure
- Design and maintain modern, scalable data architectures (data lakes, lakehouses, data mesh).
- Build and manage ETL/ELT pipelines using tools like Apache Airflow, Spark, DBT, Glue, etc.
- Structure data for LLM consumption, fine-tuning, and real-time inference.
- Define and enforce data standards, lineage, and governance policies.
⚙️ AI/ML Support Infrastructure
- Partner closely with data scientists to enable seamless model training and deployment.
- Enable real-time and batch processing for model inputs and outputs.
- Develop data ingestion pipelines for multi-modal data (text, images, audio, video, sensors).
- Support RAG architecture with optimized access to vector databases (e.g., FAISS, Pinecone, Weaviate).
🔐 Security, Compliance, and Access Management
- Implement role-based access control (RBAC), authentication protocols (OAuth2, SAML, SSO).
- Ensure data encryption, masking, and anonymization where required (GDPR, HIPAA compliance).
- Build audit-ready systems with complete logging, tracking, and rollback capabilities.
🛠️ DevOps & MLOps Integration
- Collaborate in building CI/CD pipelines for data and model deployment using Git, Docker, Kubernetes.
- Integrate with MLFlow, DVC, Airflow, and SageMaker/Vertex AI for end-to-end lifecycle tracking.
- Ensure observability, performance tuning, and failure recovery mechanisms.
📈 Scalability, Reliability & Performance
- Architect data solutions to handle large-scale, high-velocity environments.
- Tune data flows, APIs, and endpoints for low-latency AI systems (e.g., for AI agents).
- Monitor pipeline health and proactively resolve bottlenecks and anomalies.
- Bachelor’s/Master’s in Computer Science, Data Engineering, or related technical field.
- 6+ years of experience in data engineering, architecture, or infrastructure roles.
- Proficiency in SQL, Python, Spark, and distributed computing frameworks.
- Hands-on experience with cloud platforms (AWS/GCP/Azure), especially data services like Redshift, BigQuery, Snowflake.
- Experience supporting LLM-based systems, RAG pipelines, or autonomous AI workflows.
- Exposure to event-driven architecture using Kafka, Pub/Sub, or Kinesis.
- Familiarity with knowledge graphs, embeddings, and vector search.
- Understanding of zero-trust architectures, data vault modeling, or semantic layer design.
- Contributions to open-source or knowledge of infra-as-code (Terraform/CDK) is a plus.
- Be the central enabler for AI/ML innovation across teams.
- Collaborate with cutting-edge AI agents, LLMs, and MLOps teams.
- Build robust systems that support enterprise-scale automation, intelligence, and decision-making.
- Shape the data backbone for future-ready intelligent platforms.
- Competitive compensation with performance-based bonuses
- Upskilling support in GenAI, security, and cloud certifications
- Remote-first flexibility and high-ownership culture
- Direct involvement in AI transformation roadmaps
Senior Data Scientist
1 Position
AI & Agentic Intelligence Specialist
Location: [Remote / Hybrid / On-site – specify location]
Type: Full-time
Experience Required: 8+ years
Department: AI, Data Science & Innovation
We’re seeking a visionary Senior Data Scientist to lead our efforts at the convergence of traditional data science and next-generation AI technologies. This role is designed for a thought-leader and practitioner who thrives on building end-to-end intelligent systems — from data modeling to deploying AI agents and LLM-powered solutions — while ensuring enterprise-grade MLOps, security, and scalability.
This isn’t your conventional data scientist role. We’re looking for someone who thinks analytically, codes creatively, and builds AI responsibly.
✅ Core Data Science & AI
- Design, build, and optimize supervised, unsupervised, and reinforcement learning models.
- Leverage LLMs (OpenAI, Claude, LLaMA, Mistral, etc.) and fine-tune custom models for enterprise use-cases.
- Architect and deploy agentic AI workflows using frameworks like LangChain, Semantic Kernel, AutoGen, Haystack, etc.
- Innovate with AI Agents for real-world automation, orchestration, and user-level intelligence.
- Apply graph learning, NLP, time series forecasting, and causal inference for strategic business problems.
⚙️ End-to-End AI System Development
- Design production-grade pipelines from data ingestion to insight delivery.
- Ensure secure authentication, API management, and compliance-friendly deployments.
- Develop and maintain ML pipelines using tools like MLflow, Airflow, DVC, Kubeflow, SageMaker, etc.
- Build CI/CD workflows integrated with data and model lifecycle.
🔐 MLOps & Enterprise Readiness
- Own the entire ML lifecycle, including versioning, monitoring, A/B testing, and rollback strategies.
- Define and apply data governance, explainability, model auditability, and ethical AI practices.
- Collaborate cross-functionally with engineering, security, and product teams for robust integrations.
🌐 Strategic Leadership & Innovation
- Lead AI transformation initiatives and drive POCs to productization.
- Stay updated on emerging AI trends, GenAI innovations, and multi-modal AI advancements.
- Mentor junior data scientists and engineers, and build AI excellence as a culture.
- Master’s/PhD in Computer Science, Data Science, Statistics, AI, or related fields.
- 8+ years of experience in data science, AI, and production-level ML systems.
- Deep expertise in Python, PyTorch/TensorFlow, Scikit-learn, Hugging Face, and LLM APIs.
- Proven track record of deploying real-time and batch ML models at scale.
- Hands-on with AI Agents, ReAct, RAG pipelines, and vector databases (Pinecone, FAISS, Weaviate).
- Familiarity with cloud ecosystems (AWS/GCP/Azure) and Kubernetes/Docker for deployment.
- Experience with authentication and access control frameworks (OAuth2, Azure AD, etc.).
- Strong knowledge of data privacy regulations (GDPR, HIPAA) and cybersecurity implications in AI systems.
- Exposure to multi-modal AI, voice, vision, text fusion models.
- Contributions to open-source or research publications is a plus.
- High-impact work in building futuristic AI solutions.
- Freedom to experiment, fail fast, and scale boldly.
- Cross-domain collaboration — AI meets security, economics, climate, and more.
- Be a builder of the next-gen intelligent systems shaping real-world decisions.
- Competitive salary + performance bonus
- Upskilling budget for AI/ML certifications
- Access to GPU clusters and research platforms