Job Description

Job description

Key Responsibilities:

Design and develop

ML, NLP, and GenAI models

to solve real-world business problems. Build, train, and fine-tune AI models using

Python

and leading

ML frameworks

. Leverage

Azure and GCP AI/ML services

for scalable and production-ready deployment. Develop and integrate

APIs

for real-time model inference and intelligent decision-making. Handle

large-scale datasets

to extract insights and drive data-informed strategies. Work collaboratively with

Data Engineers, Software Developers, and Product Teams

to integrate AI solutions into existing products and platforms. Implement and maintain

CI/CD pipelines

for model development and deployment automation. Follow

Agile methodologies

and adhere to

software engineering best practices

. Stay current with

AI/ML innovations

, including LLMs and GenAI advancements, and incorporate them into ongoing projects. Lead

research initiatives

and contribute to innovative solution development. Provide

mentorship

to junior team members and ensure knowledge sharing. Continuously monitor and optimize models for

performance and scalability

in production.

Required Skills & Qualifications:

Proficiency in

Python

and experience with

TensorFlow, PyTorch

, or

Scikit-learn

. In-depth understanding of

NLP techniques

, such as

transformers, embeddings

, and

text analytics

. Proven expertise in

Generative AI models

(e.g.,

GPT, BERT, LLMs

). Strong experience with

Azure AI/ML services

(Azure ML, Cognitive Services, Databricks). Hands-on experience with

GCP AI/ML tools

such as

Vertex AI

,

BigQuery ML

, and

TensorFlow on GCP

. Proficient in building and integrating

REST APIs

for ML model inference. Familiarity with

CI/CD practices

tailored for ML environments. Strong grasp of

software engineering principles

(version control, modularity, testing). Experience working in

Agile development environments

. Excellent

problem-solving skills

and the ability to work in a

fast-paced, dynamic environment

. Solid background in

statistical analysis

,

data mining

, and

data visualization

.

Preferred Qualifications:

Experience with

MLOps

and end-to-end

model lifecycle automation

. Knowledge of

vector databases

and

Retrieval-Augmented Generation (RAG)

. Familiarity with

big data processing frameworks

such as

Apache Spark

and

Hadoop

. Ability to communicate complex technical concepts to

non-technical stakeholders

. Exposure to

Graph Neural Networks (GNNs)

and

recommendation engines

. Understanding of

AutoML frameworks

and advanced

hyperparameter tuning techniques

.
Job Type: Contractual / Temporary

Pay: ?700.00 - ?900.00 per hour

Schedule:

Day shift
Experience:

Python : 3 years (Required) TensorFlow: 3 years (Required) PyTorch: 3 years (Required) Scikit-learn: 3 years (Required)
Work Location: In person

Job Type: Contractual / Temporary

Pay: ?500.00 - ?700.00 per hour

Benefits:

Health insurance Provident Fund
Schedule:

Day shift Monday to Friday Weekend availability
Supplemental Pay:

Performance bonus
Work Location: In person