01
TRPD-26-05236
AI Engineer
Hiring an AI Engineer (5–10 years) to design, build, and deploy ML and Generative AI solutions (LLMs, embeddings, transformers, RAG) in production. The role requires strong Python-based ML/DL skills, experience integrating LLMs via APIs, building scalable AI services/microservices, and implementing MLOps (CI/CD, monitoring, drift, retraining). Candidates should be comfortable with data pipelines for training/inference and deploying on cloud platforms (Azure/AWS/GCP) using containers (Docker/Kubernetes) and vector databases/search for RAG.
Position summary
- Location
- India
- Workplace
- On-site
- Employment
- Full Time
- Experience
- Minimum 5 years and Maximum 10 years
Role overview
Why This Role Matters.
Hiring an AI Engineer (5–10 years) to design, build, and deploy ML and Generative AI solutions (LLMs, embeddings, transformers, RAG) in production. The role requires strong Python-based ML/DL skills, experience integrating LLMs via APIs, building scalable AI services/microservices, and implementing MLOps (CI/CD, monitoring, drift, retraining). Candidates should be comfortable with data pipelines for training/inference and deploying on cloud platforms (Azure/AWS/GCP) using containers (Docker/Kubernetes) and vector databases/search for RAG.
Your Impact
Deliver Enterprise Value
Help organisations solve complex business problems through modern technology, consulting expertise and measurable outcomes.
Collaboration
Work Across Teams
Collaborate with consultants, architects, engineers and client stakeholders throughout the project lifecycle.
Growth
Learn Continuously
Gain exposure to enterprise technologies, certifications, mentoring and real-world project experience.
Career Path
Grow With Ubique
Build a long-term consulting career with opportunities to take on greater responsibility and leadership over time.
Responsibilities
What You'll Be Doing.
Every role at Ubique contributes directly to solving meaningful business challenges for our clients.
02
Develop scalable AI services and microservices using Python, REST APIs, and cloud-native technologies.
03
Optimize models for performance, accuracy, and cost efficiency.
04
Work with structured and unstructured datasets for feature engineering, vectorization, and model training.
05
Build data pipelines for training, validation, and inference.
06
Collaborate with data engineering teams on data ingestion, storage, and governance.
07
Implement CI/CD pipelines for ML models (MLOps).
08
Monitor model performance and drift; implement retraining strategies.
09
Manage model lifecycle management, logging, and observability.
10
Integrate AI systems with enterprise applications, APIs, and cloud platforms (Azure/AWS/GCP).
11
Build Retrieval-Augmented Generation (RAG) architectures leveraging vector databases like Pinecone, FAISS, Weaviate, or Azure AI Search.
12
Ensure solutions align with enterprise security, compliance, and ethical AI standards.`
Technology stack
Tools & Technologies.
The platforms and technologies you'll use to build modern, enterprise-grade solutions.
01
Python
Required
02
NumPy
Required
03
Pandas
Required
04
PyTorch
Required
05
TensorFlow
Required
06
Transformers
Required
07
Hugging Face
Required
08
LLM
Required
09
OpenAI
Required
10
Azure OpenAI
Required
11
Anthropic
Required
12
Llama
Required
13
NLP
Required
14
embeddings
Required
15
vector embeddings
Required
16
RAG
Required
17
retrieval augmented generation
Required
18
REST API
Required
19
microservices
Required
20
Azure
Required
Requirements
Skills & Experience.
We value curiosity, collaboration and continuous learning. If you don't meet every requirement but believe you can make an impact, we'd still love to hear from you.
Essential
Required Qualifications
Python
NumPy
Pandas
PyTorch
TensorFlow
Transformers (Hugging Face/transformer architectures)
Machine learning algorithms
Deep learning
NLP
LLMs (e.g., OpenAI, Azure OpenAI, Anthropic, Llama)
Embeddings / vectorization
RAG (Retrieval-Augmented Generation) pipelines
REST APIs
Cloud platforms (Azure/AWS/GCP)
MLOps (CI/CD for ML, model lifecycle, monitoring/drift, retraining)
MLOps platforms/tools (MLflow or Kubeflow or Azure ML or SageMaker or Databricks)
Vector databases / vector search (Pinecone, FAISS, Weaviate, Chroma, Azure AI Search)
Docker
Kubernetes
Preferred
Nice to Have
Serverless compute
Logging/observability for ML services
Enterprise security/compliance/ethical AI alignment
Microservices architecture
Data governance collaboration
What you'll gain
More Than Just A Job.
We're committed to helping every team member grow professionally, personally and technically while working on meaningful projects.
Global Exposure
Collaborate with international clients and multicultural teams on enterprise programmes.
Continuous Learning
Expand your expertise through mentoring, certifications and hands-on project experience.
Career Growth
Take ownership, develop leadership skills and grow your consulting career over time.
Flexible Working
Hybrid and remote collaboration designed around trust and delivering exceptional outcomes.
People First
Join a supportive culture where collaboration, respect and long-term relationships come first.
Enterprise Projects
Work on meaningful technology initiatives for leading organisations across industries.
Apply
Apply for AI Engineer
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