8 Software Design jobs in Noida

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Job Title: ESDM (Electronic System Design and Manufacturing) Trainer

Location: Delhi

Job Description:

Position Overview:

We are looking for an experienced ESDM Trainer to lead engaging training sessions in Electronic System Design and Manufacturing. The ideal candidate will develop and implement programs that help students improve their technical skills in areas like PCB design, embedded systems, and digital electronics. Additionally, the trainer will focus on building essential soft skills such as communication, teamwork, and problem-solving. This role involves creating an interactive learning environment, monitoring student progress, and providing helpful feedback to ensure effective learning.

Roles and Responsibilities:

1. PCB Designing

2. Testing and Validation

3. Embedded systems and Designs

4. Soldering and Desoldering

5. Assembling

6. Digital Electronics

Design and develop electronic circuits using various components such as transistors, diodes, ICs, etc.

Create PCB designs for embedded systems and designs.

Conduct soldering and desoldering tasks with precision.

Teach students about electronics concepts including digital electronics, electronic circuit design, and communication engineering.

Develop lesson plans and course materials for ESDM training programs.

Impart ESDM Training:

Develop and deliver engaging and effective training sessions to students on various electronic sector, including communication, teamwork, customer service, and problem-solving and aimed at enhancing students' personality, self-confidence, and overall professionalism

Maintain Student Records:

Keep accurate and up-to-date records of students' progress, attendance, and performance in training programs.

Counsel Parents and Students:

Provide guidance and counselling to both students and parents regarding the importance of Training and their impact on future career prospects.

Collaborate with Placement Department:

Work closely with the placement department to align training programs with the skill sets required by potential employers.

Assist in preparing students for job interviews and career opportunities.

Qualifications:

Bachelor's degree in related stream.

Proven experience as ESDM Trainer or similar role.

Strong understanding of ESDM development and training methodologies.

Excellent communication and presentation skills.

Ability to inspire and motivate students to develop their electronic skills.

Strong organizational and record-keeping abilities.

Previous experience in an educational or training institution is preferred.

Company Website :

Apply on : or ,

Role: Electrical / Electronics Engineer

Industry Type: Management Consulting

Department: Production, Manufacturing & Engineering

Employment Type: Full Time, Permanent

Role Category: Engineering

Education

UG: B.Tech/B.E. in Electronics/Telecommunication

PG: M.Tech in Electronics/Telecommunication, Instrumentation, Electrical

Key Skills

Skills highlighted with are preferred keyskills

PCB DesigningCircuit DesigningElectronic Circuit DesignEmbedded systems and DesignsDigital Electronics

Communication EngineeringElectronicsElectronics And CommunicationPCBElectronic ComponentsESDMSoldering and DesolderingElectronics Circuits

Job Description:

We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning. This role combines deep technical knowledge in ML/AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response. You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized—reducing fatigue and enabling faster, more effective decision-making.

Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security platform.

Key Responsibilities

LLM Integration & Workflows:

Build, fine-tune, and integrate large language models (LLMs) into existing systems.

Develop agentic workflows for investigation, classification, and automated response in cybersecurity.

Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.

Machine Learning Development:

Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.

Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).

Data Preparation & Feature Engineering:

Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).

Engineer features to maximize model interpretability and performance.

Model Training, Evaluation, and Deployment:

Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.).

Optimize hyperparameters and fine-tune LLMs for task-specific improvements.

Deploy ML/LLM models into production at scale with strong monitoring, drift detection, and observability.

Collaboration & Documentation:

Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.

Document models, workflows, and pipelines for clarity, reproducibility, and knowledge sharing.

Requirements

Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or a related field.

5+ years of experience in ML/AI, including 3+ years deploying production-grade systems.

Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.

Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.

Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).

Hands-on experience building workflow automation with LLMs and integrating them into applications.

Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).

Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).

Experience with recommendation systems or reinforcement learning is a strong plus.

Proven track record of deploying ML/AI models into production environments with scalability in mind.

Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).

Understanding of MLOps best practices (CI/CD for ML, monitoring, retraining strategies).

Strong problem-solving and analytical mindset.

Excellent communication and teamwork skills.

Ability to work in a fast-paced, evolving startup environment.

Write to me at for more details.

Job Description:

We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning. This role combines deep technical knowledge in ML/AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response. You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized—reducing fatigue and enabling faster, more effective decision-making.

Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security platform.

Key Responsibilities

LLM Integration & Workflows:

Build, fine-tune, and integrate large language models (LLMs) into existing systems.

Develop agentic workflows for investigation, classification, and automated response in cybersecurity.

Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.

Machine Learning Development:

Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.

Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).

Data Preparation & Feature Engineering:

Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).

Engineer features to maximize model interpretability and performance.

Model Training, Evaluation, and Deployment:

Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.).

Optimize hyperparameters and fine-tune LLMs for task-specific improvements.

Deploy ML/LLM models into production at scale with strong monitoring, drift detection, and observability.

Collaboration & Documentation:

Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.

Document models, workflows, and pipelines for clarity, reproducibility, and knowledge sharing.

Requirements

Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or a related field.

5+ years of experience in ML/AI, including 3+ years deploying production-grade systems.

Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.

Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.

Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).

Hands-on experience building workflow automation with LLMs and integrating them into applications.

Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).

Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).

Experience with recommendation systems or reinforcement learning is a strong plus.

Proven track record of deploying ML/AI models into production environments with scalability in mind.

Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).

Understanding of MLOps best practices (CI/CD for ML, monitoring, retraining strategies).

Strong problem-solving and analytical mindset.

Excellent communication and teamwork skills.

Ability to work in a fast-paced, evolving startup environment.

Write to me at for more details.

Job Description:

We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning. This role combines deep technical knowledge in ML/AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response. You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized—reducing fatigue and enabling faster, more effective decision-making.

Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security platform.

Key Responsibilities

LLM Integration & Workflows:

Build, fine-tune, and integrate large language models (LLMs) into existing systems.

Develop agentic workflows for investigation, classification, and automated response in cybersecurity.

Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.

Machine Learning Development:

Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.

Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).

Data Preparation & Feature Engineering:

Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).

Engineer features to maximize model interpretability and performance.

Model Training, Evaluation, and Deployment:

Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.).

Optimize hyperparameters and fine-tune LLMs for task-specific improvements.

Deploy ML/LLM models into production at scale with strong monitoring, drift detection, and observability.

Collaboration & Documentation:

Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.

Document models, workflows, and pipelines for clarity, reproducibility, and knowledge sharing.

Requirements

Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or a related field.

5+ years of experience in ML/AI, including 3+ years deploying production-grade systems.

Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.

Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.

Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).

Hands-on experience building workflow automation with LLMs and integrating them into applications.

Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).

Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).

Experience with recommendation systems or reinforcement learning is a strong plus.

Proven track record of deploying ML/AI models into production environments with scalability in mind.

Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).

Understanding of MLOps best practices (CI/CD for ML, monitoring, retraining strategies).

Strong problem-solving and analytical mindset.

Excellent communication and teamwork skills.

Ability to work in a fast-paced, evolving startup environment.

Write to me at for more details.

Job Description:

We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning. This role combines deep technical knowledge in ML/AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response. You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized—reducing fatigue and enabling faster, more effective decision-making.

Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security platform.

Key Responsibilities

LLM Integration & Workflows:

Build, fine-tune, and integrate large language models (LLMs) into existing systems.

Develop agentic workflows for investigation, classification, and automated response in cybersecurity.

Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.

Machine Learning Development:

Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.

Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).

Data Preparation & Feature Engineering:

Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).

Engineer features to maximize model interpretability and performance.

Model Training, Evaluation, and Deployment:

Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.).

Optimize hyperparameters and fine-tune LLMs for task-specific improvements.

Deploy ML/LLM models into production at scale with strong monitoring, drift detection, and observability.

Collaboration & Documentation:

Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.

Document models, workflows, and pipelines for clarity, reproducibility, and knowledge sharing.

Requirements

Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or a related field.

5+ years of experience in ML/AI, including 3+ years deploying production-grade systems.

Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.

Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.

Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).

Hands-on experience building workflow automation with LLMs and integrating them into applications.

Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).

Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).

Experience with recommendation systems or reinforcement learning is a strong plus.

Proven track record of deploying ML/AI models into production environments with scalability in mind.

Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).

Understanding of MLOps best practices (CI/CD for ML, monitoring, retraining strategies).

Strong problem-solving and analytical mindset.

Excellent communication and teamwork skills.

Ability to work in a fast-paced, evolving startup environment.

Write to me at for more details.

Job Description:

We are seeking a highly skilled and motivated AI Engineer with expertise in large language models (LLMs), AI workflows, and machine learning. This role combines deep technical knowledge in ML/AI with hands-on experience building intelligent, production-ready systems that enhance cybersecurity investigation, prioritization, and response. You will work at the intersection of LLM-driven automation, workflow orchestration, and classical ML models to improve how alerts are prioritized, classified, and contextualized—reducing fatigue and enabling faster, more effective decision-making.

Your work will directly influence the development of agentic AI systems, workflow automation, and recommendation engines within cloud security platform.

Key Responsibilities

LLM Integration & Workflows:

Build, fine-tune, and integrate large language models (LLMs) into existing systems.

Develop agentic workflows for investigation, classification, and automated response in cybersecurity.

Apply techniques like retrieval-augmented generation (RAG), prompt engineering, and fine-tuning for domain-specific tasks.

Machine Learning Development:

Design, implement, and optimize ML models for prioritization, ranking, clustering, anomaly detection, and classification.

Apply both classical forecasting models (AR, ARIMA, SARIMA, ES) and modern architectures (XGBoost, LSTM, DeepAR, N-BEATS, Temporal Fusion Transformer).

Data Preparation & Feature Engineering:

Collect, preprocess, and transform structured and unstructured data (including logs, text, and access patterns).

Engineer features to maximize model interpretability and performance.

Model Training, Evaluation, and Deployment:

Train and evaluate models using rigorous metrics (precision, recall, AUC, F1, etc.).

Optimize hyperparameters and fine-tune LLMs for task-specific improvements.

Deploy ML/LLM models into production at scale with strong monitoring, drift detection, and observability.

Collaboration & Documentation:

Work closely with data scientists, ML engineers, security researchers, and software teams to build end-to-end solutions.

Document models, workflows, and pipelines for clarity, reproducibility, and knowledge sharing.

Requirements

Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or a related field.

5+ years of experience in ML/AI, including 3+ years deploying production-grade systems.

Experience contributing to publications (patents, libraries, or peer-reviewed papers) is a plus.

Strong knowledge of machine learning algorithms for classification, clustering, ranking, and anomaly detection.

Proficiency with LLM frameworks and APIs (OpenAI, Hugging Face Transformers, LangChain, LlamaIndex).

Hands-on experience building workflow automation with LLMs and integrating them into applications.

Solid programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn).

Knowledge of NLP tasks (text classification, summarization, embeddings, semantic search).

Experience with recommendation systems or reinforcement learning is a strong plus.

Proven track record of deploying ML/AI models into production environments with scalability in mind.

Familiarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes).

Understanding of MLOps best practices (CI/CD for ML, monitoring, retraining strategies).

Strong problem-solving and analytical mindset.

Excellent communication and teamwork skills.

Ability to work in a fast-paced, evolving startup environment.

Write to me at for more details.